-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathslide.dio
1249 lines (1249 loc) · 541 KB
/
slide.dio
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<mxfile host="65bd71144e" modified="2020-10-24T00:19:01.828Z" agent="5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Code/1.50.1 Chrome/83.0.4103.122 Electron/9.2.1 Safari/537.36" etag="FPeMah6ZHkwVzNEg9M59" version="13.6.5" pages="5">
<diagram id="BgLL_pz36sN0xVKbdkGY" name="Intro">
<mxGraphModel dx="1192" dy="760" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
<root>
<mxCell id="0"/>
<mxCell id="1" parent="0"/>
<mxCell id="2" value="<table border="1" width="100%" cellpadding="4" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><th align="center"><b style="text-align: left"><font style="font-size: 32px">Agenda</font></b><br></th></tr><tr><td style="font-size: 22px"><ol><li>Problems after train model (zzz)</li><li>Solution (zzz)</li><li>Explain "ML Explanation"</li><li>Demo &amp; Tips to use &amp; Discuss</li></ol></td></tr></tbody></table>" style="text;html=1;overflow=fill;" parent="1" vertex="1">
<mxGeometry x="250" y="160" width="380" height="230" as="geometry"/>
</mxCell>
<mxCell id="3" value="<font><span style="font-size: 32px">Machine Learning Explanation</span><br><font style="font-size: 22px"><i>Aka. Model intepretability</i></font></font>" style="text;html=1;strokeColor=#A50040;fillColor=#d80073;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontColor=#ffffff;" parent="1" vertex="1">
<mxGeometry x="131" y="10" width="600" height="140" as="geometry"/>
</mxCell>
<mxCell id="gvhKvGkfzMuIchKrfcxh-3" value="<b style="font-size: 16px">Scope</b>:<br style="font-size: 16px">- No Math (vì không rành lắm)<br style="font-size: 16px">- Focus: application on Tabular data<br>- Free style<br>&nbsp; - Slide (English &amp; Tiếng Việt mixing, dùng nhiều hình, dùng drawio để vẽ diagram dễ hơn)<br>&nbsp; - ACE cứ thoải mái hỏi lúc trình bày. <br>&nbsp; &nbsp; Đừng đợi đến cuối vì đến cuối ko nhớ mình muốn hỏi gì.<br>&nbsp; &nbsp; Nguyên sẽ cố gắng trả lời.<br><br><br><b>Requirement:</b> Biết chút chút về ML<br><i>Minimum</i>:<br>- ML model là cái gì? Nó cần data format ra sao để train/học? Nó predict ra sao?<br>- Feature, Label là gì? Linear Regresson là cái gì? etc.<br><i>Plus</i>:<br>- Nhìn code bên phải tác giả đang làm gì.<br>- Ko hiểu cũng ko sao." style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;" parent="1" vertex="1">
<mxGeometry x="240" y="555" width="660" height="330" as="geometry"/>
</mxCell>
<mxCell id="rYS14VfVzPQzquWEvspr-7" value="<b style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250)">Sharing based on:<br></b><span style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250) ; display: inline ; float: none">- Onsite/internal projects.</span><br style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250)"><span style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250) ; display: inline ; float: none">-&nbsp;Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019. https://christophm.github.io/interpretable-ml-book/.</span><br style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250)"><span style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250) ; display: inline ; float: none">-&nbsp;https://github.com/slundberg/shap</span><br style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250)"><span style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250) ; display: inline ; float: none">-&nbsp;https://github.com/SauceCat/PDPbox</span><br style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250)"><span style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250) ; display: inline ; float: none">-&nbsp;https://scikit-learn.org/stable/index.html</span>" style="text;whiteSpace=wrap;html=1;fontSize=16;fontColor=#000000;" parent="1" vertex="1">
<mxGeometry x="240" y="950" width="550" height="170" as="geometry"/>
</mxCell>
<mxCell id="5" value="<span style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; letter-spacing: normal ; text-align: left ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; background-color: rgb(248 , 249 , 250) ; display: inline ; float: none"><b>About</b><br>- @nguyentp2<br>- Experience: ML on TimeSeries data, Tabular data.<br></span>" style="text;whiteSpace=wrap;html=1;fontSize=16;fontColor=#000000;" parent="1" vertex="1">
<mxGeometry x="240" y="480" width="390" height="75" as="geometry"/>
</mxCell>
<mxCell id="tUOYV1UDl2Puowm_njTY-5" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=data:image/png,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;" parent="1" vertex="1">
<mxGeometry x="920" y="290" width="606" height="167" as="geometry"/>
</mxCell>
<mxCell id="tUOYV1UDl2Puowm_njTY-7" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=data:image/png,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;" parent="1" vertex="1">
<mxGeometry x="920" y="457" width="618" height="530" as="geometry"/>
</mxCell>
<mxCell id="tUOYV1UDl2Puowm_njTY-8" value="<b>Objective: </b>Hiểu và làm được cách Explain ML model&nbsp;" style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;" parent="1" vertex="1">
<mxGeometry x="240" y="430" width="400" height="20" as="geometry"/>
</mxCell>
<mxCell id="tUOYV1UDl2Puowm_njTY-9" value="Trình bày còn nhiều sai sót, mong nhận được đóng góp của các anh/chị/em." style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontStyle=2;fontColor=#FF3399;" parent="1" vertex="1">
<mxGeometry x="230" y="910" width="560" height="20" as="geometry"/>
</mxCell>
</root>
</mxGraphModel>
</diagram>
<diagram id="sDhYQl70FfHHl0J-c6Cd" name="Problem">
<mxGraphModel dx="681" dy="434" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
<root>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-0"/>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-1" parent="iFcQL7_CTI6N3bAQ0g9t-0"/>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-5" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontColor=#FFD966;" parent="iFcQL7_CTI6N3bAQ0g9t-1" source="iFcQL7_CTI6N3bAQ0g9t-3" target="iFcQL7_CTI6N3bAQ0g9t-4" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-3" value="<font color="#000000">Data</font>" style="shape=cylinder2;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;size=15;fontColor=#FFD966;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="120" y="100" width="60" height="80" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-7" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontColor=#000000;" parent="iFcQL7_CTI6N3bAQ0g9t-1" source="iFcQL7_CTI6N3bAQ0g9t-4" target="iFcQL7_CTI6N3bAQ0g9t-6" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-4" value="Feature Engineering" style="shape=parallelogram;perimeter=parallelogramPerimeter;whiteSpace=wrap;html=1;fixedSize=1;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="210" y="240" width="160" height="60" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-6" value="Model" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="450" y="340" width="90" height="50" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-17" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontColor=#000000;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=0;exitY=0;exitDx=0;exitDy=15;exitPerimeter=0;" parent="iFcQL7_CTI6N3bAQ0g9t-1" source="iFcQL7_CTI6N3bAQ0g9t-6" target="iFcQL7_CTI6N3bAQ0g9t-4" edge="1">
<mxGeometry relative="1" as="geometry">
<Array as="points">
<mxPoint x="60" y="355"/>
<mxPoint x="60" y="270"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-19" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontColor=#000000;entryX=-0.017;entryY=0.463;entryDx=0;entryDy=0;entryPerimeter=0;exitX=0;exitY=0;exitDx=55;exitDy=50;exitPerimeter=0;" parent="iFcQL7_CTI6N3bAQ0g9t-1" source="iFcQL7_CTI6N3bAQ0g9t-6" target="iFcQL7_CTI6N3bAQ0g9t-3" edge="1">
<mxGeometry relative="1" as="geometry">
<Array as="points">
<mxPoint x="505" y="430"/>
<mxPoint x="40" y="430"/>
<mxPoint x="40" y="137"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-32" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=22;fontColor=#FFD966;exitX=0.5;exitY=1;exitDx=0;exitDy=0;exitPerimeter=0;" parent="iFcQL7_CTI6N3bAQ0g9t-1" source="iFcQL7_CTI6N3bAQ0g9t-10" target="iFcQL7_CTI6N3bAQ0g9t-28" edge="1">
<mxGeometry relative="1" as="geometry">
<Array as="points">
<mxPoint x="700" y="480"/>
<mxPoint x="150" y="480"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-10" value="Prediction<br>and Metric" style="shape=note;whiteSpace=wrap;html=1;backgroundOutline=1;darkOpacity=0.05;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="660" y="320" width="80" height="100" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-20" value=""Awesome" Experiment workflow" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontColor=#000000;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="10" y="40" width="250" height="50" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-22" value="Khách hàng" style="shape=umlActor;verticalLabelPosition=bottom;verticalAlign=top;html=1;outlineConnect=0;fontColor=#000000;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="200" y="680" width="30" height="60" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-28" value="<font color="#ea6b66">Result</font>" style="rounded=1;whiteSpace=wrap;html=1;strokeWidth=2;fillWeight=4;hachureGap=8;hachureAngle=45;fillColor=#1ba1e2;sketch=1;fontSize=22;fontColor=#FFD966;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="100" y="525" width="120" height="60" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-29" value="Mình" style="shape=umlActor;verticalLabelPosition=bottom;verticalAlign=top;html=1;outlineConnect=0;fontColor=#000000;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="100" y="680" width="30" height="60" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-30" value="" style="shape=flexArrow;endArrow=classic;html=1;fontSize=22;fontColor=#FFD966;" parent="iFcQL7_CTI6N3bAQ0g9t-1" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="120" y="670" as="sourcePoint"/>
<mxPoint x="150" y="620" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-31" value="" style="shape=flexArrow;endArrow=classic;html=1;fontSize=22;fontColor=#FFD966;" parent="iFcQL7_CTI6N3bAQ0g9t-1" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="220" y="670" as="sourcePoint"/>
<mxPoint x="180" y="620" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-34" value="???" style="ellipse;shape=cloud;whiteSpace=wrap;html=1;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="100" y="770" width="120" height="80" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-36" value="" style="verticalLabelPosition=bottom;verticalAlign=top;html=1;shape=mxgraph.basic.x;fontSize=16;gradientColor=#ea6b66;fillColor=#f8cecc;strokeColor=#b85450;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="42.5" y="890" width="20" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-37" value="Sao lại predict sai vậy?" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="62.5" y="890" width="200" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-39" value="Sao lại predict đúng vậy?" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="62.5" y="930" width="200" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-40" value="" style="verticalLabelPosition=bottom;verticalAlign=top;html=1;shape=mxgraph.basic.tick;fontSize=16;gradientColor=#97d077;fillColor=#d5e8d4;strokeColor=#82b366;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="40" y="925" width="25" height="30" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-43" value="Sao sample A lại predict khác sample B vậy?" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="65" y="975" width="427.5" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-45" value="Data X mình nghĩ có giá trị, sao lại ko improve model nhỉ?" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="62.5" y="1020" width="425" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-47" value="KH: Tao đoán feature X này nó linear correlation. Mình: ..." style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="65" y="1065" width="425" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-49" value="Thông tin nào là quan trọng nhất đối với prediction?" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="392.5" y="900" width="425" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-51" value="?" style="whiteSpace=wrap;html=1;rounded=0;fillColor=#e6d0de;gradientColor=#d5739d;fontSize=16;align=center;strokeColor=#996185;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="372.5" y="900" width="20" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-52" value="?" style="whiteSpace=wrap;html=1;rounded=0;fillColor=#e6d0de;gradientColor=#d5739d;fontSize=16;align=center;strokeColor=#996185;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="40" y="975" width="20" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-53" value="?" style="whiteSpace=wrap;html=1;rounded=0;fillColor=#e6d0de;gradientColor=#d5739d;fontSize=16;align=center;strokeColor=#996185;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="40" y="1020" width="20" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-54" value="?" style="whiteSpace=wrap;html=1;rounded=0;fillColor=#e6d0de;gradientColor=#d5739d;fontSize=16;align=center;strokeColor=#996185;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="42.5" y="1065" width="20" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-55" value="Sao train chạy tốt mà test lại chạy tệ quá vậy?" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="392.5" y="935" width="425" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-56" value="?" style="whiteSpace=wrap;html=1;rounded=0;fillColor=#e6d0de;gradientColor=#d5739d;fontSize=16;align=center;strokeColor=#996185;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="372.5" y="935" width="20" height="20" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-57" value="???" style="ellipse;shape=cloud;whiteSpace=wrap;html=1;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="512.5" y="975" width="120" height="80" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-58" value="???" style="ellipse;shape=cloud;whiteSpace=wrap;html=1;fontSize=16;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="682.5" y="985" width="120" height="80" as="geometry"/>
</mxCell>
<mxCell id="iFcQL7_CTI6N3bAQ0g9t-59" value="Có rất nhiều thứ cần phải làm sau khi train model!<br>Nhiều nhất là: Giải thích (aka. report/docs)." style="text;html=1;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=16;fontStyle=2;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="260" y="525" width="460" height="65" as="geometry"/>
</mxCell>
<mxCell id="cA24AKzZqW2GhSNbaPjV-0" value="<span style="color: rgb(51, 51, 51); font-size: 32px; text-align: left;">Problem</span>" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=32;" parent="iFcQL7_CTI6N3bAQ0g9t-1" vertex="1">
<mxGeometry x="345" y="10" width="130" height="40" as="geometry"/>
</mxCell>
<mxCell id="_0OM4f4TWuEy4V2H6zwQ-0" value="" style="shape=flexArrow;endArrow=classic;html=1;" parent="iFcQL7_CTI6N3bAQ0g9t-1" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="570" y="369.5" as="sourcePoint"/>
<mxPoint x="650" y="369.5" as="targetPoint"/>
<Array as="points">
<mxPoint x="610" y="369.5"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="ZxsKIdzFt1yJcK1V6O1G-0" value="" style="endArrow=classic;html=1;exitX=0;exitY=0.5;exitDx=0;exitDy=0;" edge="1" parent="iFcQL7_CTI6N3bAQ0g9t-1" source="iFcQL7_CTI6N3bAQ0g9t-4" target="iFcQL7_CTI6N3bAQ0g9t-3">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="520" y="220" as="sourcePoint"/>
<mxPoint x="570" y="170" as="targetPoint"/>
<Array as="points">
<mxPoint x="120" y="230"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="03EAWEKZXxeZeVlWYxFY-0" value="đủ loại, từ đơn giản đến phức tạp<br>(white box -&gt; black box)" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;" vertex="1" parent="iFcQL7_CTI6N3bAQ0g9t-1">
<mxGeometry x="492.5" y="300" width="190" height="30" as="geometry"/>
</mxCell>
</root>
</mxGraphModel>
</diagram>
<diagram id="ReATutWyt76TFM1xgHZk" name="Solution">
<mxGraphModel dx="1192" dy="760" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
<root>
<mxCell id="_u_w697fZiYXVwy7G1AS-0"/>
<mxCell id="_u_w697fZiYXVwy7G1AS-1" parent="_u_w697fZiYXVwy7G1AS-0"/>
<mxCell id="q5bGVOwL_7TqVLtpetuL-0" value="<table border="1" width="100%" cellpadding="4" style="text-align: center ; width: 100% ; height: 100% ; border-collapse: collapse ; font-size: 15px"><tbody style="font-size: 15px"><tr style="background-color: rgb(167 , 201 , 66) ; color: rgb(255 , 255 , 255) ; border: 1px solid rgb(152 , 191 , 33) ; font-size: 15px"><th style="font-size: 15px">Problem</th><th style="font-size: 15px">&nbsp;How to solve</th></tr><tr style="border: 1px solid rgb(152 , 191 , 33) ; font-size: 15px"><td style="font-size: 15px">Thông tin nào quan trọng với prediction?</td><td style="font-size: 15px">Chỉ ra các features nào quan trọng với model.<br style="font-size: 15px">(Cái gì quan trọng?)</td></tr><tr style="background-color: rgb(234 , 242 , 211) ; border: 1px solid rgb(152 , 191 , 33) ; font-size: 15px"><td style="font-size: 15px">Feature X1 quan trọng cho prediction.<br style="font-size: 15px">Nhưng có phải quan trọng đều như nhau với các sample ko?</td><td style="font-size: 15px">Show được mối quan hệ của feature với prediction cho all samples.<br style="font-size: 15px">(Quan trọng như thế nào?)</td></tr><tr style="border: 1px solid rgb(152 , 191 , 33) ; font-size: 15px"><td style="font-size: 15px">Tại sao lại dự đoán cho sample A thế này?</td><td style="font-size: 15px">Show được các yếu tố nào + đóng góp vào prediction ra sao<br>cho từng sample cụ thể.<br>(Dựa vào đâu mà predict như vậy?)</td></tr></tbody></table>" style="text;html=1;strokeColor=#c0c0c0;fillColor=#ffffff;overflow=fill;rounded=0;fontSize=15;align=left;whiteSpace=wrap;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="40" y="140" width="770" height="400" as="geometry"/>
</mxCell>
<mxCell id="l2Z34amMcPLsX9MHL3t--0" value="<i>Solution</i>" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=32;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="140" y="770" width="190" height="20" as="geometry"/>
</mxCell>
<mxCell id="l2Z34amMcPLsX9MHL3t--1" value="Bóc tách các câu hỏi -&gt; gom nhóm -&gt; có thể gom lại thành 3 nhóm chính như bên dưới." style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="35" y="110" width="640" height="20" as="geometry"/>
</mxCell>
<mxCell id="8Vkeprv3ORBIJIZwxVyj-0" value="Tại sao lại có hỏi mấy thứ này?<br>Build model xong là được rồi chứ?" style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="150" y="564.5" width="260" height="40" as="geometry"/>
</mxCell>
<mxCell id="8Vkeprv3ORBIJIZwxVyj-1" value="" style="verticalLabelPosition=bottom;verticalAlign=top;html=1;shape=mxgraph.basic.smiley;rounded=0;fontSize=16;align=left;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="70" y="549.5" width="70" height="70" as="geometry"/>
</mxCell>
<mxCell id="8Vkeprv3ORBIJIZwxVyj-2" value="<ul><li>Tò mò (mình lẫn khách hàng). Muốn hiểu nhiều hơn về bài toán, model, data.</li><li>Debug model khi predict sai. Confirm model khi predict đúng.</li><li>An tâm hơn khi sử dụng. Nếu mình hiểu model chạy thế nào, mình control nó dễ hơn. Kh hiểu model chạy ra sao, việc nhận dc support của khách trong dự án sẽ cao hơn.</li><li>Là 1 phần tài liệu cho vào report, docs trong dự án :)</li></ul>" style="text;strokeColor=none;fillColor=none;html=1;whiteSpace=wrap;verticalAlign=middle;overflow=hidden;rounded=0;fontSize=16;align=left;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="125" y="604.5" width="600" height="155.5" as="geometry"/>
</mxCell>
<mxCell id="8Vkeprv3ORBIJIZwxVyj-5" value="" style="shape=flexArrow;endArrow=classic;html=1;fontSize=16;fontColor=#FFD966;" parent="_u_w697fZiYXVwy7G1AS-1" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="160" y="770" as="sourcePoint"/>
<mxPoint x="160" y="830" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="8Vkeprv3ORBIJIZwxVyj-6" value="Model Explanation" style="shape=process;whiteSpace=wrap;html=1;backgroundOutline=1;rounded=0;fillColor=#d80073;fontSize=16;align=left;strokeColor=#A50040;fontColor=#ffffff;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="70" y="830" width="180" height="60" as="geometry"/>
</mxCell>
<mxCell id="8Vkeprv3ORBIJIZwxVyj-7" value="Cách gọi khác: Model Interpretability." style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontStyle=2" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="35" y="900" width="280" height="20" as="geometry"/>
</mxCell>
<mxCell id="i0JA56_9sl2Um7bOKcMB-0" value="<b style="font-size: 16px ; font-style: italic">Chống chỉ định:<br></b><span style="font-size: 16px">- Không phải là lời giải cho tất cả các câu hỏi. Tại sao AB test lại tệ hơn control group? Sao model không lên production dc? Cái này khó. Có những câu hỏi, problem nằm ở phần data collection. Explain có thể giúp điều tra.<br>- Model Explanation không hoàn hảo so với bài toán thực tế. <b><font color="#0000ff">Nhưng xài được. </font></b>Cần phải hiểu để sử dụng đúng.<br>- Không phải lúc nào cũng cần. Ví dụ: Low impact environment (reccomend song).<br><br>Extend: security, ko thể explain tại sao lại reject hồ sơ bảo hiểm đến user, nhưng internal thì DS/DA cần.</span>" style="rounded=0;whiteSpace=wrap;html=1;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=13;fontColor=#000000;align=left;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry x="40" y="930" width="700" height="220" as="geometry"/>
</mxCell>
<mxCell id="i0JA56_9sl2Um7bOKcMB-1" value="Solution" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=32;" parent="_u_w697fZiYXVwy7G1AS-1" vertex="1">
<mxGeometry y="30" width="190" height="20" as="geometry"/>
</mxCell>
<mxCell id="6xNkHrpZnZjU4Lz_Oes1-0" value="1vd tại sao explain giúp mình debug<br>https://towardsdatascience.com/interpretability-in-machine-learning-ab0cf2e66e1" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;" vertex="1" parent="_u_w697fZiYXVwy7G1AS-1">
<mxGeometry x="820" y="700" width="440" height="30" as="geometry"/>
</mxCell>
<mxCell id="FcURY9fvV2o3LOWI3Svc-0" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=https://miro.medium.com/max/838/1*483oxEmAQKqTji9tZM2VCw.png;" vertex="1" parent="_u_w697fZiYXVwy7G1AS-1">
<mxGeometry x="810" y="740.5" width="419" height="339" as="geometry"/>
</mxCell>
<mxCell id="FcURY9fvV2o3LOWI3Svc-2" value="<span style="color: rgb(41 , 41 , 41) ; font-family: &#34;charter&#34; , &#34;georgia&#34; , &#34;cambria&#34; , &#34;times new roman&#34; , &#34;times&#34; , serif ; font-size: 12px ; letter-spacing: -0.063px ; background-color: rgb(255 , 255 , 255)">...at the University of Washington. The researches trained an image recognition model to classify animals as a husky or wolf. .... In Figure 2, we can see that the model was basing its predictions on image backgrounds. If the background had snow, the animal was always classified as a wolf. They had essentially built a model that detects snow.</span>" style="whiteSpace=wrap;html=1;fontSize=12;" vertex="1" parent="_u_w697fZiYXVwy7G1AS-1">
<mxGeometry x="1270" y="880" width="120" height="60" as="geometry"/>
</mxCell>
</root>
</mxGraphModel>
</diagram>
<diagram id="YqB-n6eQWLkOqfZdYuOF" name="Explanation">
<mxGraphModel dx="1402" dy="894" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
<root>
<mxCell id="HnbC79WsU8X0Fww-gVYV-0"/>
<mxCell id="HnbC79WsU8X0Fww-gVYV-1" parent="HnbC79WsU8X0Fww-gVYV-0"/>
<mxCell id="xU06GIs4kWv2_fyeo8BF-1" value="ML Explanation" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=32;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="70" y="20" width="230" height="40" as="geometry"/>
</mxCell>
<mxCell id="xU06GIs4kWv2_fyeo8BF-2" value="Feature Importance (Global)" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=32;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="75" y="1190" width="420" height="40" as="geometry"/>
</mxCell>
<mxCell id="xU06GIs4kWv2_fyeo8BF-3" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=data:image/png,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;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="27" y="1240" width="382.02" height="460" as="geometry"/>
</mxCell>
<mxCell id="xU06GIs4kWv2_fyeo8BF-5" value="Sắp xếp các features theo mức độ quan trọng đối với model (hoặc với prediction)." style="text;strokeColor=none;fillColor=none;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="430" y="1250" width="376" height="110" as="geometry"/>
</mxCell>
<mxCell id="xU06GIs4kWv2_fyeo8BF-6" value="Trục Y: Features, quan trọng ở top hoặc bottom.<br>Trục X: Độ quan trọng" style="text;strokeColor=none;fillColor=none;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="430" y="1360" width="366" height="70" as="geometry"/>
</mxCell>
<mxCell id="xU06GIs4kWv2_fyeo8BF-7" value="Đây là thông tin tổng quát, summarize model nên hay được classify là: Global Explanation." style="text;strokeColor=none;fillColor=none;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="430" y="1420" width="320" height="70" as="geometry"/>
</mxCell>
<mxCell id="U189lxSioX0C9oLbpb0j-1" value="Có nhiều model -&gt; Explanation Technique cũng khác (Model Specific).<br>Tuy nhiên: Có những technique xài được cho hầu hết các model (Model Agnostic).<br><b><br>Tips</b>:<br>- Refer Model Agnostic when we can.<br>- Use Model Specific when we must." style="text;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="70" y="910" width="640" height="150" as="geometry"/>
</mxCell>
<mxCell id="U189lxSioX0C9oLbpb0j-29" value="Key takeaway: Nếu làm sai giá trị của feature X thì score thay đổi ra sao." style="text;strokeColor=#6c8ebf;fillColor=#dae8fc;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="90" y="2250" width="550" height="70" as="geometry"/>
</mxCell>
<mxCell id="U189lxSioX0C9oLbpb0j-30" value="Code" style="label;whiteSpace=wrap;html=1;image=img/clipart/Gear_128x128.png;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="440" y="1850" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="U189lxSioX0C9oLbpb0j-31" value="How it works?" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="676" y="1680" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="vxqfzL9Lb8hIuzOkD-Va-0" value="&nbsp;When it does not works and how to fix." style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="20" y="1890" width="330" height="80" as="geometry"/>
</mxCell>
<mxCell id="Vz2YbPk7zIjAy92Qtt-n-4" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="HnbC79WsU8X0Fww-gVYV-1" source="vxqfzL9Lb8hIuzOkD-Va-1" target="Vz2YbPk7zIjAy92Qtt-n-3" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="vxqfzL9Lb8hIuzOkD-Va-1" value="<b><font color="#ff3333">Permutation Problem</font></b><br>Nhiều features "strongly correlated" (Multi-Collinear).<br style="font-size: 13px">Fix:<br style="font-size: 13px">- Detect Collinear features (Corr. Heatmap, Feature Dendrogram) -&gt; chọn lại 1 features trong nhóm.<br style="font-size: 13px">- Nên chọn lọc feature cẩn thận." style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;labelBackgroundColor=none;fontSize=13;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="30" y="2060" width="350" height="80" as="geometry"/>
</mxCell>
<mxCell id="vxqfzL9Lb8hIuzOkD-Va-2" value="Khi remove feature nhưng metric giảm không đúng với chart?<br style="font-size: 14px">Fix:<br style="font-size: 14px">- Check "strongly correlated" problem.<br>- Check error bar!" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;labelBackgroundColor=none;fontSize=14;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="430" y="1970" width="350" height="90" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-1" value="Cách tính với Model Specific:<br>- Linear model: Xem weight.<br>- Tree model: Check "information gain" khi split 1 feature" style="text;strokeColor=none;fillColor=none;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="430" y="1520" width="320" height="70" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-2" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;exitX=0;exitY=0;exitDx=175;exitDy=80;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="vxqfzL9Lb8hIuzOkD-Va-0" target="vxqfzL9Lb8hIuzOkD-Va-1" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="460" y="1960" as="sourcePoint"/>
<mxPoint x="170" y="2050" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-3" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=0;exitY=0;exitDx=175;exitDy=80;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="vxqfzL9Lb8hIuzOkD-Va-0" target="vxqfzL9Lb8hIuzOkD-Va-2" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="460" y="1960" as="sourcePoint"/>
<mxPoint x="510" y="1910" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-4" value="Partial Dependence Plot (Global)" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=32;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="40" y="2360" width="480" height="40" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-5" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=data:image/png,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;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="40" y="2470" width="344.12" height="230" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-6" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=data:image/png,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;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="400" y="2450" width="386" height="238.61" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-7" value="Pros and Cons" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="710" y="1860" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-8" value="<span style="font-size: 16px ; text-align: center">Permutation Feature Importance</span><br style="font-size: 16px ; text-align: center"><span style="font-size: 16px ; text-align: center">(Model Agnostic)</span>" style="rounded=0;whiteSpace=wrap;html=1;labelBackgroundColor=none;fillColor=#dae8fc;fontSize=14;align=left;strokeColor=#6c8ebf;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="80" y="1720" width="270" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-9" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=0;entryDy=20;entryPerimeter=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-8" target="U189lxSioX0C9oLbpb0j-31" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="460" y="1830" as="sourcePoint"/>
<mxPoint x="510" y="1780" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-10" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-8" target="U189lxSioX0C9oLbpb0j-30" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="460" y="1830" as="sourcePoint"/>
<mxPoint x="510" y="1780" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-11" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=55;entryDy=0;entryPerimeter=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-8" target="BJFQ_aLWZ-yjW4ppOOWF-7" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="460" y="1830" as="sourcePoint"/>
<mxPoint x="510" y="1780" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-12" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=155;entryDy=0;entryPerimeter=0;exitX=0.5;exitY=1;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-8" target="vxqfzL9Lb8hIuzOkD-Va-0" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="460" y="1830" as="sourcePoint"/>
<mxPoint x="510" y="1780" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-40" value="" style="group" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1" connectable="0">
<mxGeometry x="990" y="1350" width="1010" height="410" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-41" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse ; font-size: 14px"><tbody style="font-size: 14px"><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px" color="#ff3399">F1</font></b></td><td align="center" style="font-size: 14px"><font style="font-size: 14px"><b style="font-size: 14px">F2</b></font></td><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px">F3</font></b></td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><font style="font-size: 14px">A</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">1</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">4</font></td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><font style="font-size: 14px">B</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">2</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">5</font></td></tr><tr style="font-size: 14px"><td style="text-align: center ; font-size: 14px"><font style="font-size: 14px">C</font></td><td style="text-align: center ; font-size: 14px"><font style="font-size: 14px">3</font></td><td style="text-align: center ; font-size: 14px"><font style="font-size: 14px">6</font></td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=14;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="10" y="50" width="130" height="150" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-42" value="Model" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="180" y="110" width="90" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-43" value="" style="endArrow=classic;html=1;entryX=0;entryY=0;entryDx=0;entryDy=15;entryPerimeter=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" source="BJFQ_aLWZ-yjW4ppOOWF-41" target="BJFQ_aLWZ-yjW4ppOOWF-42" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="210" y="350" as="sourcePoint"/>
<mxPoint x="260" y="300" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-44" value="Predict" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="260" y="100" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-45" value="" style="endArrow=classic;html=1;exitX=0;exitY=0;exitDx=90;exitDy=20;exitPerimeter=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" source="BJFQ_aLWZ-yjW4ppOOWF-42" target="BJFQ_aLWZ-yjW4ppOOWF-46" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="190" y="310" as="sourcePoint"/>
<mxPoint x="540" y="310" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-46" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td>Prediction</td></tr><tr><td>0.5</td></tr><tr><td>0.2</td></tr><tr><td>0.1</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="360" y="60" width="60" height="140" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-47" value="Score original = 0.8" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=1;fontSize=15;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="500" y="125" width="140" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-48" value="" style="endArrow=classic;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.25;entryDx=0;entryDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" source="BJFQ_aLWZ-yjW4ppOOWF-46" target="BJFQ_aLWZ-yjW4ppOOWF-47" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="280" y="140" as="sourcePoint"/>
<mxPoint x="370" y="140" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-49" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse ; font-size: 14px"><tbody style="font-size: 14px"><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px" color="#ff3399">F1</font></b></td><td align="center" style="font-size: 14px"><font style="font-size: 14px"><b style="font-size: 14px">F2</b></font></td><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px">F3</font></b></td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><font style="font-size: 14px">B</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">1</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">4</font></td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><font style="font-size: 14px">C</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">2</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">5</font></td></tr><tr style="font-size: 14px"><td style="text-align: center ; font-size: 14px">A</td><td style="text-align: center ; font-size: 14px"><font style="font-size: 14px">3</font></td><td style="text-align: center ; font-size: 14px"><font style="font-size: 14px">6</font></td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=14;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="10" y="260" width="130" height="150" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-50" value="Model" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="180" y="320" width="90" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-51" value="" style="endArrow=classic;html=1;entryX=0;entryY=0;entryDx=0;entryDy=15;entryPerimeter=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" source="BJFQ_aLWZ-yjW4ppOOWF-49" target="BJFQ_aLWZ-yjW4ppOOWF-50" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="210" y="560" as="sourcePoint"/>
<mxPoint x="260" y="510" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-52" value="Predict" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="260" y="310" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-53" value="" style="endArrow=classic;html=1;exitX=0;exitY=0;exitDx=90;exitDy=20;exitPerimeter=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" source="BJFQ_aLWZ-yjW4ppOOWF-50" target="BJFQ_aLWZ-yjW4ppOOWF-54" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="190" y="520" as="sourcePoint"/>
<mxPoint x="540" y="520" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-54" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td>Prediction</td></tr><tr><td>0.1</td></tr><tr><td>0.3</td></tr><tr><td>0.9</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="360" y="270" width="60" height="140" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-55" value="Score new = 0.7" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=1;fontSize=15;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="500" y="335" width="140" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-56" value="" style="endArrow=classic;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.25;entryDx=0;entryDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" source="BJFQ_aLWZ-yjW4ppOOWF-54" target="BJFQ_aLWZ-yjW4ppOOWF-55" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="280" y="350" as="sourcePoint"/>
<mxPoint x="370" y="350" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-57" value="Permute Feature F1" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=1;fontSize=15;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry y="230" width="140" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-58" value="<span>Score change = Score original - Score new</span>" style="rounded=0;whiteSpace=wrap;html=1;strokeWidth=1;fontSize=15;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="710" y="315" width="300" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-59" value="" style="endArrow=classic;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" source="BJFQ_aLWZ-yjW4ppOOWF-55" target="BJFQ_aLWZ-yjW4ppOOWF-58" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="430" y="350" as="sourcePoint"/>
<mxPoint x="510" y="350" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-60" value="" style="endArrow=classic;html=1;exitX=1;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" source="BJFQ_aLWZ-yjW4ppOOWF-47" target="BJFQ_aLWZ-yjW4ppOOWF-58" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="650" y="355" as="sourcePoint"/>
<mxPoint x="720" y="355" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-61" value="Permutation Feature Importance for F1" style="text;strokeColor=none;fillColor=none;html=1;fontSize=24;fontStyle=1;verticalAlign=middle;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="360" width="100" height="40" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-62" value="Original data" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=1;fontSize=15;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="5" y="20" width="140" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-63" value="already trained model" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="180" y="170" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-64" value="already trained model" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="180" y="380" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="SXkjF6jH2DYDkNtT3qKa-1" value="Score có thể là 1 metric bất kỳ: Accuray, AUC, F1, MSE, LogLoss, etc..." style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="490" y="90" width="390" height="20" as="geometry"/>
</mxCell>
<mxCell id="VZpbNj2VVynpA9PSCskr-8" value="Evaluate" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="400" y="140" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="VZpbNj2VVynpA9PSCskr-10" value="Evaluate" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-40" vertex="1">
<mxGeometry x="400" y="350" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-65" value="" style="group" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1" connectable="0">
<mxGeometry x="910" y="1820" width="330" height="260" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-14" value="<p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><strong><font color="#ea6b66">Pros</font></strong></p><ul style="margin-top: 0px ; margin-bottom: 0.7em ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><li class="code-line" style="position: relative">No need to retrain model for each feature.</li><li class="code-line" style="position: relative">Model Agnostic.</li><li class="code-line" style="position: relative">Easy to code and explain the result.</li></ul>" style="text;whiteSpace=wrap;html=1;fontSize=14;fontColor=#000000;" parent="BJFQ_aLWZ-yjW4ppOOWF-65" vertex="1">
<mxGeometry width="330" height="110" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-15" value="<p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><strong><font color="#97d077">Cons</font></strong><br></p><ul style="margin-top: 0px ; margin-bottom: 0.7em ; color: rgb(56 , 58 , 66)"><li class="code-line" style="position: relative">Permutation problem.</li><li class="code-line" style="position: relative">Need True label.</li><li class="code-line" style="position: relative">Train or Test?</li><li class="code-line" style="position: relative">Phải có model score ngon 1 chút.</li></ul>" style="text;whiteSpace=wrap;html=1;fontSize=14;fontColor=#000000;" parent="BJFQ_aLWZ-yjW4ppOOWF-65" vertex="1">
<mxGeometry y="140" width="330" height="120" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-69" value="<meta charset="utf-8"><span style="color: rgb(56, 58, 66); font-size: 14px; font-style: normal; font-weight: 400; letter-spacing: normal; text-indent: 0px; text-transform: none; word-spacing: 0px; display: inline; float: none;">Feature Importance can show which feature has strongest effect on prediction. But we are interested more details like: how is the relationship between feature and prediction? Is it linear, or non-linear? Does lower value of feature and higher value of feature affect prediction in similar way? etc. And Partial Dependence Plot can help us about this.</span>" style="text;whiteSpace=wrap;html=1;fontSize=14;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="70" y="2710" width="550" height="100" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-70" value="<font style="font-size: 16px">Key takeaway:&nbsp;For each value of feature, we need to know the average prediction.</font>" style="text;strokeColor=#82b366;fillColor=#d5e8d4;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;labelBackgroundColor=none;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="115" y="3300" width="665" height="70" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-71" value="Code" style="label;whiteSpace=wrap;html=1;image=img/clipart/Gear_128x128.png;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="440" y="2930" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-72" value="How it works?" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="720" y="2800" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-73" value="&nbsp;When it does not works and how to fix." style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="27" y="2930" width="330" height="80" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-74" value="Face same "Permutation problem" as in Permutation Feature Importance.&nbsp;" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;labelBackgroundColor=none;fontSize=13;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="30" y="3100" width="240" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-76" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;exitX=0;exitY=0;exitDx=175;exitDy=80;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-73" target="BJFQ_aLWZ-yjW4ppOOWF-74" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="467" y="3000" as="sourcePoint"/>
<mxPoint x="177" y="3090" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-77" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0.5;entryY=0;entryDx=0;entryDy=0;exitX=0;exitY=0;exitDx=175;exitDy=80;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-73" target="BJFQ_aLWZ-yjW4ppOOWF-125" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="467" y="3000" as="sourcePoint"/>
<mxPoint x="405" y="3115" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-78" value="Pros and Cons" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="666" y="2923" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-79" value="<span style="font-size: 16px ; text-align: center">Partial Dependence Plot<br>(Model Agnostic)</span>" style="rounded=0;whiteSpace=wrap;html=1;labelBackgroundColor=none;fillColor=#d5e8d4;fontSize=14;align=left;strokeColor=#82b366;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="75" y="2810" width="270" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-80" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=0;entryDy=20;entryPerimeter=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-79" target="BJFQ_aLWZ-yjW4ppOOWF-72" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="467" y="2870" as="sourcePoint"/>
<mxPoint x="517" y="2820" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-81" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-79" target="BJFQ_aLWZ-yjW4ppOOWF-71" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="467" y="2870" as="sourcePoint"/>
<mxPoint x="517" y="2820" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-82" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=55;entryDy=0;entryPerimeter=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-79" target="BJFQ_aLWZ-yjW4ppOOWF-78" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="467" y="2870" as="sourcePoint"/>
<mxPoint x="517" y="2820" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-83" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=155;entryDy=0;entryPerimeter=0;exitX=0.5;exitY=1;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-79" target="BJFQ_aLWZ-yjW4ppOOWF-73" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="467" y="2870" as="sourcePoint"/>
<mxPoint x="517" y="2820" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-84" value="Ứng với từng giá trị của feature, thì prediction thay đổi ra sao.<br>Nói cách khác: Prediction thay đổi ra sao trên 1 range các giá trị của feature.<br>Chính xác hơn: Ứng với giá trị của feature, Avg. Prediction là bao nhiêu?" style="text;strokeColor=none;fillColor=none;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="850" y="2360" width="376" height="110" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-85" value="Trục Y: Average Prediction ứng với giá trị ở trục X.<br>Trục X: Giá trị của feature (có thể là Numerical hay Categorical)" style="text;strokeColor=none;fillColor=none;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="850" y="2490" width="366" height="70" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-86" value="Đây là thông tin tổng quát, summarize feature của model nên hay được classify là: Global Explanation." style="text;strokeColor=none;fillColor=none;html=1;fontSize=16;fontStyle=0;verticalAlign=middle;align=left;rounded=0;whiteSpace=wrap;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="850" y="2580" width="320" height="70" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-88" value="" style="group" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1" connectable="0">
<mxGeometry x="940" y="2650" width="1050" height="420" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-89" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse ; font-size: 14px"><tbody style="font-size: 14px"><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px" color="#ff3399">F1</font></b></td><td align="center" style="font-size: 14px"><font style="font-size: 14px"><b style="font-size: 14px">F2</b></font></td><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px">F3</font></b></td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><font style="font-size: 14px">A</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">1</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">4</font></td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><font style="font-size: 14px">B</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">2</font></td><td align="center" style="font-size: 14px"><font style="font-size: 14px">5</font></td></tr><tr style="font-size: 14px"><td style="text-align: center ; font-size: 14px"><font style="font-size: 14px">C</font></td><td style="text-align: center ; font-size: 14px"><font style="font-size: 14px">3</font></td><td style="text-align: center ; font-size: 14px"><font style="font-size: 14px">6</font></td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=14;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry y="170" width="130" height="150" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-90" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse ; font-size: 14px"><tbody style="font-size: 14px"><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px" color="#ff3399">F1</font></b></td><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px">F2</font></b></td><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px">F3</font></b></td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px">B</td><td align="center" style="font-size: 14px">1</td><td align="center" style="font-size: 14px">4</td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px">B</td><td align="center" style="font-size: 14px">2</td><td align="center" style="font-size: 14px">5</td></tr><tr style="font-size: 14px"><td style="text-align: center ; font-size: 14px">B</td><td style="text-align: center ; font-size: 14px">3</td><td style="text-align: center ; font-size: 14px">6</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=14;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="190" y="250" width="130" height="110" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-91" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-89" target="BJFQ_aLWZ-yjW4ppOOWF-90" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-92" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse ; font-size: 14px"><tbody><tr><td align="center"><b><font style="font-size: 14px" color="#ff3399">F1</font></b></td><td align="center"><b><font style="font-size: 14px">F2</font></b></td><td align="center"><b><font style="font-size: 14px">F3</font></b></td></tr><tr><td align="center"><font style="font-size: 14px">A</font></td><td align="center"><font style="font-size: 14px">1</font></td><td align="center"><font style="font-size: 14px">4</font></td></tr><tr><td align="center"><font style="font-size: 14px">A</font></td><td align="center"><font style="font-size: 14px">2</font></td><td align="center"><font style="font-size: 14px">5</font></td></tr><tr><td style="text-align: center"><font style="font-size: 14px">A</font></td><td style="text-align: center"><font style="font-size: 14px">3</font></td><td style="text-align: center"><font style="font-size: 14px">6</font></td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="190" y="80" width="130" height="110" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-93" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-89" target="BJFQ_aLWZ-yjW4ppOOWF-92" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-94" value="Model" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="400" y="110" width="90" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-95" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-92" target="BJFQ_aLWZ-yjW4ppOOWF-94" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-96" value="Model" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="400" y="280" width="90" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-97" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-90" target="BJFQ_aLWZ-yjW4ppOOWF-96" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-98" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td>Prediction</td></tr><tr><td>0.5</td></tr><tr><td>0.2</td></tr><tr><td>0.1</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="560" y="65" width="60" height="140" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-99" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-94" target="BJFQ_aLWZ-yjW4ppOOWF-98" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-100" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td>Prediction</td></tr><tr><td>0.8</td></tr><tr><td>0.3</td></tr><tr><td>0.3</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="560" y="235" width="60" height="140" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-101" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-96" target="BJFQ_aLWZ-yjW4ppOOWF-100" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-102" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td align="center">Prediction</td></tr><tr><td align="center">0.23</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="680" y="92.5" width="60" height="85" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-103" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-98" target="BJFQ_aLWZ-yjW4ppOOWF-102" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-104" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td align="center">Prediction</td></tr><tr><td align="center">0.53</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="680" y="262.5" width="60" height="85" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-105" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-100" target="BJFQ_aLWZ-yjW4ppOOWF-104" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-106" value="Repeat for other values in feature F1..." style="shape=ext;double=1;rounded=0;whiteSpace=wrap;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="230" y="384" width="490" height="36" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-107" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-89" target="BJFQ_aLWZ-yjW4ppOOWF-106" edge="1">
<mxGeometry relative="1" as="geometry">
<Array as="points">
<mxPoint x="160" y="245"/>
<mxPoint x="160" y="402"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-108" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse ; font-size: 14px"><tbody style="font-size: 14px"><tr style="font-size: 14px"><td align="center" style="font-size: 14px"><b style="font-size: 14px"><font style="font-size: 14px">F1</font></b></td><td align="center" style="font-size: 14px"><font style="font-size: 14px"><b style="font-size: 14px">Predictions</b></font></td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px">A</td><td align="center" style="font-size: 14px">0.23</td></tr><tr style="font-size: 14px"><td align="center" style="font-size: 14px">B</td><td align="center" style="font-size: 14px">0.53</td></tr><tr style="font-size: 14px"><td style="text-align: center ; font-size: 14px">C</td><td style="text-align: center ; font-size: 14px">0.2</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=14;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="850" y="140" width="170" height="150" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-109" value="" style="endArrow=classic;html=1;fontSize=14;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-102" target="BJFQ_aLWZ-yjW4ppOOWF-108" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="650" y="300" as="sourcePoint"/>
<mxPoint x="700" y="250" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-110" value="" style="endArrow=classic;html=1;fontSize=14;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-104" target="BJFQ_aLWZ-yjW4ppOOWF-108" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="750" y="145" as="sourcePoint"/>
<mxPoint x="860" y="225" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-111" value="" style="endArrow=classic;html=1;fontSize=14;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" source="BJFQ_aLWZ-yjW4ppOOWF-106" target="BJFQ_aLWZ-yjW4ppOOWF-108" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="750" y="315" as="sourcePoint"/>
<mxPoint x="860" y="225" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-112" value="Set "A" to F1" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="190" y="60" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-113" value="Set "B" to F1" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="200" y="220" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-114" value="Avg all predictions" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="655" y="65" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-115" value="Avg all predictions" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="655" y="242.5" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-116" value="PDP for F1" style="text;strokeColor=none;fillColor=none;html=1;fontSize=24;fontStyle=1;verticalAlign=middle;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="310" width="180" height="40" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-117" value="How prediction changes for among values of features." style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="840" y="300" width="210" height="20" as="geometry"/>
</mxCell>
<mxCell id="LdDVU0bMz6xmH4-eEKZj-0" value="already trained model" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="390" y="170" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="LdDVU0bMz6xmH4-eEKZj-1" value="already trained model" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=13;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-88" vertex="1">
<mxGeometry x="400" y="340" width="110" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-118" value="" style="group" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1" connectable="0">
<mxGeometry x="960" y="3110" width="720" height="230" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-119" value="<p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><strong><font color="#ea6b66">Pros</font></strong></p><ul style="margin-top: 0px ; margin-bottom: 0.7em ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><li class="code-line" style="position: relative">Easy to code and explain.</li><li class="code-line" style="position: relative">Model Agnostic.</li><li class="code-line" style="position: relative">No need label.</li><li class="code-line" style="position: relative"><span style="font-family: , , &#34;segoe wpc&#34; , &#34;segoe ui&#34; , , &#34;ubuntu&#34; , &#34;droid sans&#34; , sans-serif">Has Causal Interpretation&nbsp;</span><em style="font-family: , , &#34;segoe wpc&#34; , &#34;segoe ui&#34; , , &#34;ubuntu&#34; , &#34;droid sans&#34; , sans-serif">in model prediction</em><span style="font-family: , , &#34;segoe wpc&#34; , &#34;segoe ui&#34; , , &#34;ubuntu&#34; , &#34;droid sans&#34; , sans-serif">. Nếu F1=A thì predict thế này, nếu F1=B thì predict thế này...</span><br></li></ul>" style="text;whiteSpace=wrap;html=1;fontSize=14;fontColor=#000000;" parent="BJFQ_aLWZ-yjW4ppOOWF-118" vertex="1">
<mxGeometry width="720" height="97.3076923076923" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-120" value="<p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><strong><font color="#97d077">Cons</font></strong><br></p><ul style="margin-top: 0px ; margin-bottom: 0.7em ; color: rgb(56 , 58 , 66)"><li class="code-line" style="position: relative">Permutation problem.</li><li class="code-line" style="position: relative">Only plot with 1 or 2 features.</li></ul>" style="text;whiteSpace=wrap;html=1;fontSize=14;fontColor=#000000;" parent="BJFQ_aLWZ-yjW4ppOOWF-118" vertex="1">
<mxGeometry y="123.84615384615384" width="720" height="106.15384615384615" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-121" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=0;entryDy=0;exitX=0;exitY=0;exitDx=130;exitDy=40;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-78" target="BJFQ_aLWZ-yjW4ppOOWF-120" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="780" y="3160" as="sourcePoint"/>
<mxPoint x="830" y="3110" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-122" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=0;exitY=0;exitDx=130;exitDy=40;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-72" target="BJFQ_aLWZ-yjW4ppOOWF-116" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="780" y="3030" as="sourcePoint"/>
<mxPoint x="830" y="2980" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-123" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=0;entryDy=0;exitX=0;exitY=0;exitDx=75;exitDy=60;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-7" target="BJFQ_aLWZ-yjW4ppOOWF-15" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="780" y="1960" as="sourcePoint"/>
<mxPoint x="830" y="1910" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-124" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;exitX=0;exitY=0;exitDx=130;exitDy=40;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="U189lxSioX0C9oLbpb0j-31" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="780" y="1960" as="sourcePoint"/>
<mxPoint x="970" y="1600" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-125" value="Dễ dàng rút ra kết luận sai về Average Prediction của 1 value.<br>Fix:<br>- Plot Confidence Interval, plot sample histogram đi kèm.<br>- Plot all samples." style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;labelBackgroundColor=none;fontSize=13;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="335" y="3150" width="295" height="110" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-126" value="Shapley and Local Explanation" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=32;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="45" y="3540" width="450" height="40" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-127" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=https://raw.githubusercontent.com/slundberg/shap/master/docs/artwork/shap_header.png;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="45" y="3610" width="583.86" height="320" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-128" value="<i>https://raw.githubusercontent.com/slundberg/shap/master/docs/artwork/shap_header.png</i>" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=14;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="30" y="3950" width="570" height="20" as="geometry"/>
</mxCell>
<mxCell id="Vz2YbPk7zIjAy92Qtt-n-8" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-129" target="Vz2YbPk7zIjAy92Qtt-n-7" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-129" value="<p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">Why I refer Shapley as main explanation tools:</p><p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; font-size: 14px ; font-style: normal ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><b><font color="#ff3399">Pros</font></b></p><ul style="margin-top: 0px ; margin-bottom: 0.7em ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><li class="code-line" style="position: relative">Ready to use<span>&nbsp;</span><a href="https://github.com/slundberg/shap" title="https://github.com/slundberg/shap" style="text-decoration: none">shap</a><span>&nbsp;</span>library in Python. This library has a lot of cool stuffs. Actively developed.</li><li class="code-line" style="position: relative">Provide global and local explaination (Feature Importance, Feature Interaction, PDP, Local Explanation). In my opinion, using same method to explain a model from top to bottom reduces a lot of communication cost. And it makes interpretation consistent!</li><li class="code-line" style="position: relative">Have solid theory (Paper claim như thế. Dùng để reference khi cần).</li></ul>" style="text;whiteSpace=wrap;html=1;fontSize=14;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="35" y="3990" width="665" height="140" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-130" value="https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance_multicollinear.html#handling-multicollinear-features" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=14;fontColor=#000000;fontStyle=2" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="409.02" y="2220" width="860" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-131" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=https://raw.githubusercontent.com/slundberg/shap/master/docs/artwork/boston_summary_plot_bar.png;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="30" y="4190" width="379.56" height="300" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-132" value="Feature Importance" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;fontStyle=1" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="142.24" y="4490" width="160" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-133" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=https://raw.githubusercontent.com/slundberg/shap/master/docs/artwork/boston_dependence_plot.png;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="452" y="4204" width="429.83" height="286" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-134" value="Partial Dependence Plot" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;fontStyle=1" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="546.83" y="4490" width="200" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-137" value="How each feature contributes to prediction for this sample?<br>Assume: chỉ có 2 features." style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;labelBackgroundColor=none;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="550" y="4880" width="440" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-138" value="https://github.com/slundberg/shap" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="40" y="3680" width="260" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-149" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=16;fontColor=#000000;exitX=0;exitY=0.5;exitDx=0;exitDy=0;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-146" target="BJFQ_aLWZ-yjW4ppOOWF-148" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-146" value="<span style="color: rgb(0 , 0 , 0) ; font-family: &#34;helvetica&#34; ; font-size: 16px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-align: center ; text-indent: 0px ; text-transform: none ; word-spacing: 0px ; display: inline ; float: none">Key takeaway: Shap value is the contribution (+/-) to average prediction.</span>" style="rounded=0;whiteSpace=wrap;html=1;labelBackgroundColor=none;fillColor=#e6d0de;gradientColor=#d5739d;fontSize=16;align=left;strokeColor=#996185;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="30" y="5210" width="490" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-148" value="<span style="color: rgb(56 , 58 , 66) ; font-family: , , &#34;segoe wpc&#34; , &#34;segoe ui&#34; , , &#34;ubuntu&#34; , &#34;droid sans&#34; , sans-serif ; font-size: 14px">What is average prediction? the average of label.</span>" style="whiteSpace=wrap;html=1;rounded=0;fontSize=16;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="115" y="5290" width="365" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-150" value="<meta charset="utf-8"><ul style="margin-top: 0px; margin-bottom: 0.7em; color: rgb(56, 58, 66); font-size: 14px; font-style: normal; font-weight: 400; letter-spacing: normal; text-indent: 0px; text-transform: none; word-spacing: 0px;"><li class="code-line" style="position: relative">For example: Target is house price. Average house price in dataset is $300K. Pick a sample with feature "Number of room"=3 rooms. Its Shapley value is 10,000. This means: "Number of room"=3 adds 10,000 to average house price. Is is<span>&nbsp;</span><strong>not</strong><span>&nbsp;</span>the change in prediction when we remove number of room = 3 from features. Intuitively, "Number of room"=3 raises prediction from $300K to $310K.</li><li class="code-line" style="position: relative">Shapley can be negative. This means feaure value substracts some prediction amount from average prediction.</li></ul>" style="text;whiteSpace=wrap;html=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="10" y="5360" width="550" height="140" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-154" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-151" target="BJFQ_aLWZ-yjW4ppOOWF-153" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-151" value="Starting prediction khi chưa biết giá trị feature = Average Prediction = 4518" style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="551" y="4950" width="540" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-152" value="Humid ~ 90" style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="719.02" y="5005" width="100" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-156" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-153" target="BJFQ_aLWZ-yjW4ppOOWF-155" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-153" value="-1200" style="whiteSpace=wrap;html=1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=center;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="591" y="5000" width="120" height="30" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-159" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-155" target="BJFQ_aLWZ-yjW4ppOOWF-158" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-155" value="-600" style="whiteSpace=wrap;html=1;rounded=0;fontSize=16;fillColor=none;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="591" y="5060" width="120" height="25" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-157" value="weathersit=RAIN/SNOW/STORM" style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="719.02" y="5065" width="250" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-158" value="Update Prediction = 4518 - 1200 - 600 = 2718" style="whiteSpace=wrap;html=1;rounded=0;fontSize=16;fillColor=none;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="591" y="5150" width="410" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-160" value="" style="group;fontSize=16;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1" connectable="0">
<mxGeometry x="1105.4" y="5080" width="940" height="900" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-161" value="" style="rounded=1;whiteSpace=wrap;html=1;align=left;dashed=1;strokeWidth=3;fillColor=#d5e8d4;strokeColor=#82b366;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry y="180" width="920" height="610" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-162" value="" style="rounded=1;whiteSpace=wrap;html=1;align=left;fillColor=#D4E1F5;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="10" y="320" width="850" height="380" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-163" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody style="font-size: 14px"><tr><td><b>Near Park</b></td><td><b>House Area</b></td><td><b><font color="#ff3399">Cat?</font></b></td><td><b>Floor</b></td></tr><tr><td>Yes</td><td>20</td><td>Yes</td><td>3</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=12;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="90" y="30" width="300" height="100" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-164" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody style="font-size: 14px"><tr><td><b>Cat</b></td><td><b>House Area</b></td></tr><tr><td>Yes</td><td>20</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=12;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="150" y="220" width="200" height="70" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-165" value="Randomly pick some features except "CAT". e.g. "House Area"<br>Intuitively: Build a core team include "Cat" and other features.<br><br>Bước này đa dạng hoá các team -&gt; đánh giá ảnh hưởng của Cat=Yes trong nhiều điều kiện khác nhau." style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=12;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="360" y="230" width="510" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-166" value="Problem: Predict a house's price.<br>This is a sample house. There are 4 features." style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=15;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="50" width="320" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-167" value="" style="shape=flexArrow;endArrow=classic;html=1;fontSize=12;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="250.5" y="160" as="sourcePoint"/>
<mxPoint x="249.5" y="210" as="targetPoint"/>
<Array as="points">
<mxPoint x="250.5" y="180"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-168" value="Calculate Shapley value for feature value: Cat?=Yes." style="text;html=1;strokeColor=#d6b656;fillColor=#fff2cc;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=12;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="60" y="130" width="360" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-169" value="Except "CAT" and chosen features, randomly fill other features values by randomly chose from dataset.<br>- Fill Floor = 2<br>- Fill Near Park = Yes<br><br>(Có đội hình core rồi, các vị trí khác cứ random)" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=12;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="100" y="455" width="530" height="75" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-170" value="Model" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="445" y="375" width="90" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-171" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td>Prediction</td></tr><tr><td>100,000</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="590" y="370" width="60" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-172" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" source="BJFQ_aLWZ-yjW4ppOOWF-170" target="BJFQ_aLWZ-yjW4ppOOWF-171" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-173" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody style="font-size: 14px"><tr><td><b>Near Park</b></td><td><b>House Area</b></td><td><b>Cat?</b></td><td><b>Floor</b></td></tr><tr><td><font color="#ff3399">Yes</font></td><td>20</td><td>Yes</td><td><font color="#ff3399">2</font></td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=12;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="100" y="350" width="300" height="100" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-174" value="Create a team with "Cat OK"" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontStyle=3" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="360" y="190" width="200" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-175" value="Step 1" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontStyle=3" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="60" y="455" width="40" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-176" value="Randomly fill value of&nbsp; "Cat" feature.&nbsp;Change "Cat" to No. It could Yes or no.<br>(Thử bỏ CAT Yes ra khỏi core team xem thế nào?)" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=top;whiteSpace=wrap;rounded=0;fontSize=12;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="70" y="650" width="500" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-177" value="Model" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="440" y="565" width="90" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-178" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" source="BJFQ_aLWZ-yjW4ppOOWF-164" target="BJFQ_aLWZ-yjW4ppOOWF-173" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-179" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=12;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" source="BJFQ_aLWZ-yjW4ppOOWF-173" target="BJFQ_aLWZ-yjW4ppOOWF-170" edge="1">
<mxGeometry relative="1" as="geometry">
<mxPoint x="490" y="280" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-180" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td>Prediction</td></tr><tr><td>90,000</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="590" y="560" width="60" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-181" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody style="font-size: 14px"><tr><td><b>Near Park</b></td><td><b>House Area</b></td><td><b>Cat?</b></td><td><b>Floor</b></td></tr><tr><td><font color="#ff3399">Yes</font></td><td>20</td><td><font color="#006633">No</font></td><td><font color="#ff3399">2</font></td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;fontSize=12;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="100" y="540" width="300" height="100" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-182" value="Step 2" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontStyle=3" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="30" y="660" width="40" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-183" value="<table border="1" width="100%" style="width: 100% ; height: 100% ; border-collapse: collapse"><tbody><tr><td>Delta change</td></tr><tr><td>100,000 - 90,000 = 10,000</td></tr></tbody></table>" style="text;html=1;strokeColor=none;fillColor=none;overflow=fill;align=center;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="655" y="462.5" width="170" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-184" value="Contribution of "Cat Yes" is 10,000" style="text;html=1;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontStyle=2;fillColor=#f8cecc;strokeColor=#b85450;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="650" y="530" width="180" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-185" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" source="BJFQ_aLWZ-yjW4ppOOWF-177" target="BJFQ_aLWZ-yjW4ppOOWF-180" edge="1">
<mxGeometry relative="1" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-186" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;entryX=1;entryY=0.526;entryDx=0;entryDy=0;entryPerimeter=0;endArrow=block;endFill=1;strokeWidth=3;startArrow=classic;startFill=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" source="BJFQ_aLWZ-yjW4ppOOWF-162" target="BJFQ_aLWZ-yjW4ppOOWF-162" edge="1">
<mxGeometry relative="1" as="geometry">
<mxPoint x="950" y="560" as="targetPoint"/>
<Array as="points">
<mxPoint x="445" y="740"/>
<mxPoint x="890" y="740"/>
<mxPoint x="890" y="520"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-187" value="Repeat to get better Contribution estimate" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="590" y="710" width="140" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-188" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=12;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" source="BJFQ_aLWZ-yjW4ppOOWF-181" target="BJFQ_aLWZ-yjW4ppOOWF-177" edge="1">
<mxGeometry relative="1" as="geometry">
<mxPoint x="490" y="480" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-189" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;entryX=1;entryY=0.5;entryDx=0;entryDy=0;startArrow=classic;startFill=1;endArrow=block;endFill=1;strokeWidth=3;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" source="BJFQ_aLWZ-yjW4ppOOWF-161" target="BJFQ_aLWZ-yjW4ppOOWF-161" edge="1">
<mxGeometry relative="1" as="geometry">
<Array as="points">
<mxPoint x="460" y="840"/>
<mxPoint x="940" y="840"/>
<mxPoint x="940" y="485"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-190" value="Repeat to create many "Cat" teams to get better Contribution estimate" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="515" y="810" width="290" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-191" value="" style="shape=flexArrow;endArrow=classic;html=1;fontSize=12;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="250.5" y="810" as="sourcePoint"/>
<mxPoint x="249.5" y="860" as="targetPoint"/>
<Array as="points">
<mxPoint x="250.5" y="830"/>
</Array>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-192" value="Shap Cat Yest = Average all Contributions" style="text;html=1;strokeColor=#b85450;fillColor=#f8cecc;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=12;fontStyle=2" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="100" y="870" width="300" height="30" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-193" value="Randomize the rest of team" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontStyle=3" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="70" y="330" width="190" height="20" as="geometry"/>
</mxCell>
<mxCell id="iNP4HNAn_sDNPrTG-3EH-4" value="simulation nhiều core team" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;" parent="BJFQ_aLWZ-yjW4ppOOWF-160" vertex="1">
<mxGeometry x="580" y="840" width="160" height="20" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-194" value="Code" style="label;whiteSpace=wrap;html=1;image=img/clipart/Gear_128x128.png;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="413" y="5750" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-212" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=16;fontColor=#000000;entryX=-0.001;entryY=0.475;entryDx=0;entryDy=0;entryPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-195" target="BJFQ_aLWZ-yjW4ppOOWF-161" edge="1">
<mxGeometry relative="1" as="geometry">
<mxPoint x="866" y="5570" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-195" value="How it works?" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="656" y="5540" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-196" value="&nbsp;When it does not works and how to fix." style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="30" y="5750" width="330" height="80" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-197" value="Face same "Permutation problem" as in Permutation Feature Importance.&nbsp;" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;labelBackgroundColor=none;fontSize=13;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="27" y="5910" width="240" height="50" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-198" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;exitX=0;exitY=0;exitDx=175;exitDy=80;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-196" target="BJFQ_aLWZ-yjW4ppOOWF-197" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="440" y="5820" as="sourcePoint"/>
<mxPoint x="150" y="5910" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-199" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0.5;entryY=0;entryDx=0;entryDy=0;exitX=0;exitY=0;exitDx=175;exitDy=80;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-196" target="BJFQ_aLWZ-yjW4ppOOWF-210" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="440" y="5820" as="sourcePoint"/>
<mxPoint x="378" y="5935" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-200" value="Pros and Cons" style="shape=cube;whiteSpace=wrap;html=1;boundedLbl=1;backgroundOutline=1;darkOpacity=0.05;darkOpacity2=0.1;rounded=0;labelBackgroundColor=none;fillColor=none;gradientColor=none;fontSize=16;fontColor=#000000;align=left;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="639" y="5743" width="130" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-201" value="<span style="font-size: 16px ; text-align: center">SHAP (Model Agnostic)</span>" style="rounded=0;whiteSpace=wrap;html=1;labelBackgroundColor=none;fillColor=#d5e8d4;fontSize=14;align=left;strokeColor=#82b366;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="65" y="5530" width="270" height="60" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-202" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=0;entryDy=20;entryPerimeter=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-201" target="BJFQ_aLWZ-yjW4ppOOWF-195" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="440" y="5690" as="sourcePoint"/>
<mxPoint x="490" y="5640" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-203" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0.5;entryDx=0;entryDy=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-201" target="BJFQ_aLWZ-yjW4ppOOWF-194" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="440" y="5690" as="sourcePoint"/>
<mxPoint x="490" y="5640" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-204" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=55;entryDy=0;entryPerimeter=0;exitX=1;exitY=0.5;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-201" target="BJFQ_aLWZ-yjW4ppOOWF-200" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="440" y="5690" as="sourcePoint"/>
<mxPoint x="490" y="5640" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-205" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0;entryY=0;entryDx=155;entryDy=0;entryPerimeter=0;exitX=0.5;exitY=1;exitDx=0;exitDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-201" target="BJFQ_aLWZ-yjW4ppOOWF-196" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="440" y="5690" as="sourcePoint"/>
<mxPoint x="490" y="5640" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-206" value="" style="group" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1" connectable="0">
<mxGeometry x="650" y="5780" width="330" height="230" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-208" value="<p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px"><strong><font color="#97d077">Cons</font></strong><br></p><ul style="margin-top: 0px ; margin-bottom: 0.7em ; color: rgb(56 , 58 , 66)"><li class="code-line" style="position: relative">Permutation problem.</li><li class="code-line" style="position: relative">Slow to calculate</li><li class="code-line" style="position: relative">Shap Python library iện tại chỉ hổ trợ: sklearn model, xgboost, TF, catboost, lightgbm, pyspark model, code bằng python.</li><li class="code-line" style="position: relative">Nếu stack, ensemble thì ko xài dc.</li></ul>" style="text;whiteSpace=wrap;html=1;fontSize=14;fontColor=#000000;" parent="BJFQ_aLWZ-yjW4ppOOWF-206" vertex="1">
<mxGeometry y="123.84615384615384" width="330" height="106.15384615384615" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-209" value="" style="endArrow=classic;html=1;fontSize=14;fontColor=#000000;entryX=0.25;entryY=0;entryDx=0;entryDy=0;exitX=0;exitY=0;exitDx=75;exitDy=60;exitPerimeter=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="BJFQ_aLWZ-yjW4ppOOWF-200" target="BJFQ_aLWZ-yjW4ppOOWF-208" edge="1">
<mxGeometry width="50" height="50" relative="1" as="geometry">
<mxPoint x="753" y="5980" as="sourcePoint"/>
<mxPoint x="803" y="5930" as="targetPoint"/>
</mxGeometry>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-210" value="Slow!<br>Fix:<br>- Sample data để tính SHAP. Compromise runtime và độ chính xác của explaination." style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;labelBackgroundColor=none;fontSize=13;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="330" y="6010" width="295" height="80" as="geometry"/>
</mxCell>
<mxCell id="BJFQ_aLWZ-yjW4ppOOWF-213" value="<font color="#ff3333">+ contribution</font>: Positive effect.<br><font color="#0000ff">- contribution:</font> Negative effect." style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="546.25" y="4818" width="230" height="40" as="geometry"/>
</mxCell>
<mxCell id="vErlHv3T4UipGBsN2hDt-0" value="Local Explanation" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;fontStyle=1" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="155" y="4710" width="150" height="20" as="geometry"/>
</mxCell>
<mxCell id="vErlHv3T4UipGBsN2hDt-2" value="Above explanation shows key steps in Shapley calculation. The most important part!<br>How this calculation transform to Contribution to Average Prediction, see mathematic description in book "Interpretable Machine Learning", Chapter 5.9, 5.10." style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="1150" y="6020" width="1130" height="40" as="geometry"/>
</mxCell>
<mxCell id="vErlHv3T4UipGBsN2hDt-3" value="https://christophm.github.io/interpretable-ml-book/" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=16;fontColor=#000000;fontStyle=2" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="1140" y="6070" width="370" height="20" as="geometry"/>
</mxCell>
<mxCell id="Vz2YbPk7zIjAy92Qtt-n-1" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=https://www.sqlshack.com/wp-content/uploads/2019/09/sample-of-a-decision-tree.png;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="711" y="44.85" width="397" height="235.15" as="geometry"/>
</mxCell>
<mxCell id="Vz2YbPk7zIjAy92Qtt-n-2" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=#ffffff;verticalAlign=top;aspect=fixed;imageAspect=0;image=https://miro.medium.com/max/1400/1*FfP7Pte4NXcj0r1v663OyA.png;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="711" y="290" width="385" height="247.29" as="geometry"/>
</mxCell>
<mxCell id="Vz2YbPk7zIjAy92Qtt-n-3" value="Nhiều features "strongly correlated" (Multi-Collinear). Ví dụ: 2 features Chiều cao &amp; cân nặng. Permutation trên chiều cao có thể tạo ra 1 sample cao 0.5m (children) nhưng nặng 65kg -&gt; unrealistic sample -&gt; unrealistic conclusion." style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;labelBackgroundColor=none;fontSize=13;fontColor=#000000;fontStyle=2" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="50" y="2170" width="350" height="80" as="geometry"/>
</mxCell>
<mxCell id="Vz2YbPk7zIjAy92Qtt-n-7" value="<p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">Consistence nghĩa là từ global đến local, cách từng feature ảnh hưởng đến predict là giống nhau.&nbsp;</p><p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">Thử hình dung, từ Feature importance xuống PDP mình xài 2 kiểu khác nhau:</p><p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">- P.FIMP chỉ ra phá nát 1 feature thay đổi metric bao nhiêu.</p><p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">- Nhưng PDP chỉ ra 1 giá trị của feature có avg. predict bao nhiêu.</p><p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">Ảnh hưởng đến predict lại dc hiểu theo 2 nghĩa khác nhau. Vậy người dùng hỏi nên ưu tiên cái nào? Cái nào đúng hơn? Tuy mình có thể hiểu dc nhưng giải thích cho người dùng rất khó.</p><p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">Nếu bắt buộc phải xài thì nếu ưu tiên xài theo kiểu:&nbsp;</p><p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">- FIMP: Ranking feature theo độ quan trọng chứ ko cần giải thích chi tiết: Feature quan trọng vị trí 1 làm thay đổi predict/metric ra sao.</p><p class="code-line" style="margin-top: 0px ; margin-bottom: 0.7em ; position: relative ; color: rgb(56 , 58 , 66) ; font-size: 14px ; font-style: normal ; font-weight: 400 ; letter-spacing: normal ; text-indent: 0px ; text-transform: none ; word-spacing: 0px">- PDP: Nên nhìn theo trend thôi.</p>" style="text;whiteSpace=wrap;html=1;fontSize=14;fontColor=#000000;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="786" y="3930" width="665" height="140" as="geometry"/>
</mxCell>
<mxCell id="Vz2YbPk7zIjAy92Qtt-n-9" value="Đây là 1 sample. Cần dự đoán liệu Sample thu nhập trên $50K." style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="110" y="5150" width="350" height="20" as="geometry"/>
</mxCell>
<mxCell id="SXkjF6jH2DYDkNtT3qKa-2" value="Hoán vị (permute) các giá trị của F1<br>Loosely speaking: <br>- Phá vỡ relationship của F1 và Target trong dataset thì prediction tốt hay xấu?<br>- Làm sai giá trị feature F1 trong dataset." style="text;html=1;align=left;verticalAlign=middle;resizable=0;points=[];autosize=1;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="1000" y="1765" width="430" height="60" as="geometry"/>
</mxCell>
<mxCell id="VZpbNj2VVynpA9PSCskr-5" value="Công thức thời học sinh ~ y = ax + b" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=22;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="1096" y="310" width="370" height="30" as="geometry"/>
</mxCell>
<mxCell id="VZpbNj2VVynpA9PSCskr-6" value="Tree ~ Rule base automation" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=22;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="1130" y="44.849999999999994" width="300" height="30" as="geometry"/>
</mxCell>
<mxCell id="VZpbNj2VVynpA9PSCskr-11" value="<font style="font-size: 16px">Data Titanic, dự đoán %Survived dựa trên giới tính, giá vé (Fare), etc...</font>" style="text;html=1;align=center;verticalAlign=middle;resizable=0;points=[];autosize=1;fontSize=22;" parent="HnbC79WsU8X0Fww-gVYV-1" vertex="1">
<mxGeometry x="55" y="2430" width="520" height="30" as="geometry"/>
</mxCell>
<mxCell id="KOn_pymRIn_FyY6k9KTp-3" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;fontSize=16;entryX=0;entryY=0.5;entryDx=0;entryDy=0;" parent="HnbC79WsU8X0Fww-gVYV-1" source="KOn_pymRIn_FyY6k9KTp-0" target="KOn_pymRIn_FyY6k9KTp-4" edge="1">
<mxGeometry relative="1" as="geometry">
<mxPoint x="1170" y="3370" as="targetPoint"/>