v1.0.2

Fixes

• #637 fix problem with font loading for report

v1.0.1

Fixes

• #634 fix problem with categorical values in target and nan values for fairness metric
• #635 add tests for fairness feature
• #636 switch off shap exceptions printouts

v1.0.0

We add support for fairness aware training in our AutoML.

• For implementation details please check issue #612
• For example usage please check article https://mljar.com/blog/fairness-machine-learning/

0.11.5

Bug fixes and updates

• #595 replace boston example dataset with California housing dataset, replace mse metric with squared_error for tree based algorithms from sklearn
• #596 change the import method for dtreeviz package

0.11.4

Fixes

• #590 dynamically set font in a report, thanks @yairVanti!

0.11.3

Unpin shap version #551

0.11.2

Enhancements

• #523 Add type hints to AutoML class, thank you @DanielR59
• #519 save train&validation index to file in train/test split, thanks @filipsPL @MaciekEO

Bug fixes

• #496 fix exception in baseline mode, thanks @DanielR59 @moshe-rl
• #522 fixed requirements issue, thanks @DanielR59 @MaciekEO
• #514 remove warning, thanks @MaciekEO
• #511 disable EDA, thanks @MaciekEO

0.11.0

Bug fixes

• #463 change multiprocessing to Parallel with loky
• #462 handle large data for tree visualization in regression
• #419 remove/hide warnings
• #411 loose dependencies for numpy and scipy

0.10.4

Enhancements

• #81 add scatter plot predicted vs target in regression
• #158 add ROC curve for binary classification
• #336 add visualization for Optuna results
• #352 add support for Colab
• #374 update seaborn
• #378 set golden features number
• #379 switch off boost_on_errors step in Optuna mode
• #380 add custom cross validation strategy
• #386 add correlation heatmap
• #387 add residual plot
• #389 add feature importance heatmap
• #390 add custom eval metric
• #393 update sklearn

Bug fixes

• #308 fix error in kaggle kernel
• #353, #355, #366, #368, #376, #382, #383, #384 fixes

Docs

• #391 add info about hyperparameters optimization methods

Big thank you for help for: @ecoskian, @xuzhang5788, @xiaobo, @RafaD5, @drorhilman, @strelzoff-erdc, @muxuezi, @tresoldi THANK YOU !!!

0.10.3

Enhancements

• #343 set seed in Optuna
• #344 set eval_metric directly in all algorithms
• #350 add estimated train time in Optuna mode
• #342 add optuna_verbose param in AutoML()
• #354 add KNN in Optuna
• #356 and Neural Network in Optuna
• #357, #348 use mljar wrapper for Random Forest and Extra Trees
• #358 add extra_tree param in LightGBM
• #359 switch off feature engineering in Optuna mode - only highly tuned models are produced
• #361 list all eval_metric in error message
• #362 add accuracy eval_metric
• #340 support for r2

Bug fixes

• #347 dont include Optuna tuning time in total_time_limit
• #360 missing auc scores for training in CatBoost