From b9e9f7dfd7aff85536f6dc55c704dcdf41f78458 Mon Sep 17 00:00:00 2001 From: Tsai Yi Lin Date: Tue, 8 Oct 2024 20:13:22 +0800 Subject: [PATCH] Deployed 8b9dfda with MkDocs version: 1.0.4 --- .../index.html" | 4 +- 10.KNN/index.html | 4 +- 11.SVM/index.html | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- 15.XGBoost/index.html | 4 +- 16.Stacking/index.html | 4 +- 17.LightGBM/index.html | 4 +- 18.CatBoost/index.html | 4 +- 19.AutoML/index.html | 4 +- .../index.html" | 4 +- 20.Auto-Sklearn/index.html | 4 +- 21.Optuna/index.html | 4 +- 22.Plotly-Express/index.html | 4 +- .../index.html" | 4 +- .../index.html" | 20 ++--- .../index.html" | 73 ++++++++++++----- .../image/img26-4.png" | Bin 79276 -> 76968 bytes .../index.html" | 76 ++++++++++-------- .../index.html" | 9 ++- .../index.html" | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- 404.html | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- .../index.html" | 4 +- google0a1389a852a5fccd.html | 1 + index.html | 4 +- ...\231\250\345\255\270\347\277\222_v2.1.pdf" | Bin 16397187 -> 16421707 bytes search/search_index.json | 2 +- sitemap.xml | 60 +++++++------- sitemap.xml.gz | Bin 214 -> 213 bytes 38 files changed, 198 insertions(+), 155 deletions(-) create mode 100644 google0a1389a852a5fccd.html diff --git "a/1.\345\205\250\346\260\221\347\230\213AI\347\263\273\345\210\2272.0\347\233\256\346\250\231\344\273\213\347\264\271/index.html" "b/1.\345\205\250\346\260\221\347\230\213AI\347\263\273\345\210\2272.0\347\233\256\346\250\231\344\273\213\347\264\271/index.html" index 8ec638f..2cf05c7 100644 --- "a/1.\345\205\250\346\260\221\347\230\213AI\347\263\273\345\210\2272.0\347\233\256\346\250\231\344\273\213\347\264\271/index.html" +++ "b/1.\345\205\250\346\260\221\347\230\213AI\347\263\273\345\210\2272.0\347\233\256\346\250\231\344\273\213\347\264\271/index.html" @@ -91,7 +91,7 @@
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diff --git a/10.KNN/index.html b/10.KNN/index.html index c7ab50b..6c23511 100644 --- a/10.KNN/index.html +++ b/10.KNN/index.html @@ -91,7 +91,7 @@
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diff --git "a/14.\351\232\250\346\251\237\346\243\256\346\236\227/index.html" "b/14.\351\232\250\346\251\237\346\243\256\346\236\227/index.html" index 8e992a9..5546c37 100644 --- "a/14.\351\232\250\346\251\237\346\243\256\346\236\227/index.html" +++ "b/14.\351\232\250\346\251\237\346\243\256\346\236\227/index.html" @@ -91,7 +91,7 @@
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diff --git "a/23.\350\263\207\346\226\231\345\210\206\345\270\203\350\210\207\351\233\242\347\276\244\345\200\274\350\231\225\347\220\206/index.html" "b/23.\350\263\207\346\226\231\345\210\206\345\270\203\350\210\207\351\233\242\347\276\244\345\200\274\350\231\225\347\220\206/index.html" index 3edafeb..c71f652 100644 --- "a/23.\350\263\207\346\226\231\345\210\206\345\270\203\350\210\207\351\233\242\347\276\244\345\200\274\350\231\225\347\220\206/index.html" +++ "b/23.\350\263\207\346\226\231\345\210\206\345\270\203\350\210\207\351\233\242\347\276\244\345\200\274\350\231\225\347\220\206/index.html" @@ -91,7 +91,7 @@
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diff --git "a/24.\344\270\215\350\203\275\345\277\275\350\246\226\347\232\204\351\201\216\346\223\254\345\220\210\350\210\207\346\254\240\346\223\254\345\220\210/index.html" "b/24.\344\270\215\350\203\275\345\277\275\350\246\226\347\232\204\351\201\216\346\223\254\345\220\210\350\210\207\346\254\240\346\223\254\345\220\210/index.html" index b8be886..3cb25ee 100644 --- "a/24.\344\270\215\350\203\275\345\277\275\350\246\226\347\232\204\351\201\216\346\223\254\345\220\210\350\210\207\346\254\240\346\223\254\345\220\210/index.html" +++ "b/24.\344\270\215\350\203\275\345\277\275\350\246\226\347\232\204\351\201\216\346\223\254\345\220\210\350\210\207\346\254\240\346\223\254\345\220\210/index.html" @@ -91,7 +91,7 @@
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如何選擇最佳的模型?

接著我們拿測試資料進行模型預測,可以發現由於紅色虛線的模型已經完整記住了訓練集的趨勢,因此在新的沒看過的資料表現就沒有那麼好了。尤其是在兩類別分隔線附近的資料最能看出端倪。於是我們可以很確定紅色虛線的模型已經過度擬合訓練資料了。另外紅色實現的模型雖然在訓練集中有幾筆會預測錯誤,但是它再測試集資料中一樣保持穩定的預測能力。

從上述的例子我們可以得知,在訓練模型時並非訓練集的誤差越小越好。我們必須同時拿測試集驗證模型的預測能力,目標是訓練集與測試集的平均誤差要越近越好。

-

一個適當的機器學習工作流程包括: -- 切割訓練集與測試集 -- 資料視覺化與前處理 -- 尋找適合的模型 -- 調整模型超參數 -- 使用適當的指標評估模型 -- 交叉驗證模型

+

一個適當的機器學習工作流程包括:

+
    +
  • 切割訓練集與測試集
  • +
  • 資料視覺化與前處理
  • +
  • 尋找適合的模型
  • +
  • 調整模型超參數
  • +
  • 使用適當的指標評估模型
  • +
  • 交叉驗證模型
  • +

Overfitting vs. Underfitting

過度擬合的反義就是欠擬合,從字面上可以得知模型預測能力是不好的。當模型太簡單時會發生欠擬合,或是加入太多的 L1/L2 正則化限制模型預測能力,使模型在從數據集中學習時變得不靈活。一個過於簡單的模型在預測中往往具有較小的方差(variance)而導致偏差(bias)就會變大。相反的過於複雜的模型會有較的變異進而導致方差大,同時偏差會變小。偏差和方差都是機器學習中的預測誤差的方式。在一般情況下我們可以減少偏差所引起的誤差,但可能會導致增加方差引起的誤差,反之亦然。

diff --git "a/25.\344\272\244\345\217\211\351\251\227\350\255\211 Cross-Validation \347\260\241\344\273\213/index.html" "b/25.\344\272\244\345\217\211\351\251\227\350\255\211 Cross-Validation \347\260\241\344\273\213/index.html" index 907f8a3..978cc8c 100644 --- "a/25.\344\272\244\345\217\211\351\251\227\350\255\211 Cross-Validation \347\260\241\344\273\213/index.html" +++ "b/25.\344\272\244\345\217\211\351\251\227\350\255\211 Cross-Validation \347\260\241\344\273\213/index.html" @@ -91,7 +91,7 @@
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Holdout Method

此方法是最經典且最簡單實作的交叉驗證法,Holdout 顧名思義就是將資料切出一部分作為模型評估的依據。在這種方法中,我們將資料隨機分為三部分:訓練集、驗證集和測試集。其中只有訓練集資料實際參與訓練,其餘的資料僅拿來評估模型好壞。驗證集使用時機是在訓練過程中可以檢視訓練的趨勢,若有發現過擬合擬合跡象可以提早發現並解決。以及方便我們進行調整超參數以及選擇最佳的模型。當然僅透過驗證集不能代表全部,因此最後確定好模型時。我們會再拿事先切好的測試集進行最終的評估,檢視模型的泛化能力。

參考

-

優點: -1. 簡單實作。 -2. 驗證集可以被拿來評估模型在訓練過程中的學習成果。 -3. 測試集可以評估模型泛化能力。

-

缺點: -1. 當資料集變異量較大時,驗證集與測試集可能無法足以評估模型。 -2. 不適合用在資料不平衡的資料集。

+

優點:

+
    +
  1. 簡單實作。
  2. +
  3. 驗證集可以被拿來評估模型在訓練過程中的學習成果。
  4. +
  5. 測試集可以評估模型泛化能力。
  6. +
+

缺點:

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    +
  1. 當資料集變異量較大時,驗證集與測試集可能無法足以評估模型。
  2. +
  3. 不適合用在資料不平衡的資料集。
  4. +

K-fold Cross-Validation

上一個方法雖然簡單,但是在訓練過程中僅切一份驗證集往往不能夠代表全部。因此我們可以透過一些技巧切割驗證集,使得訓練過程中有一個更公正的評估方式。我們可以透過 K-Fold 方法將訓練資料再依序切割訓練集與測試集,K-Fold 裡面的測試集可以當成驗證集。K-Fold 的方法中 K 是由我們自由調控的,在每次的迭代中會選擇一組作為驗證集,其餘 (k-1) 組作為訓練集。透過這種方式學習,不同分組訓練的結果進行平均來減少方差,因此模型的性能對數據的劃分就不會那麼敏感。

參考

-

優點: -1. 降低模型訓練對於資料集的偏差。 -2. 訓練集與驗證集完整被充分利用與學習。

-

缺點: -1. 不適合用於資料不平衡的資料集。 -2. 如果要簡單的 K-fold 來尋找超參數會有資料洩漏問題導致訓練結果有偏差,因為在每個 Fold 中都會使用同一組資料進行驗證。 -3. 在相同的驗證集計算模型的誤差,當找到了最佳的超參數。這可能會導致重大偏差,有過擬合擬合疑慮。

+

優點:

+
    +
  1. 降低模型訓練對於資料集的偏差。
  2. +
  3. 訓練集與驗證集完整被充分利用與學習。
  4. +
+

缺點:

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    +
  1. 不適合用於資料不平衡的資料集。
  2. +
  3. 如果要簡單的 K-fold 來尋找超參數會有資料洩漏問題導致訓練結果有偏差,因為在每個 Fold 中都會使用同一組資料進行驗證。
  4. +
  5. 在相同的驗證集計算模型的誤差,當找到了最佳的超參數。這可能會導致重大偏差,有過擬合擬合疑慮。
  6. +

Leave One Out

此方法是 K-fold 其中一種特例,當 K 等於資料集的數量時就等於 Leave One Out 方法。也就是在每次訓練時僅會把一筆資料當成測試資料,其餘的 N-1 筆資料作為訓練模型的資料。此作法相當簡單明瞭,但是訓練負擔會非常重且耗時。然而 Leave p-out 是另一種技巧,其中的 p 使用者可以自己設定每次訓練需要留幾筆資料作為測試集。

-

優點: -1. 簡單且容易理解,好實作。

-

缺點: -1. 需要花費更多的訓練時間。

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優點:

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  1. 簡單且容易理解,好實作。
  2. +
+

缺點:

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  1. 需要花費更多的訓練時間。
  2. +

Random Subsampling

+

Random Subsampling 方法是一種簡單且常用的交叉驗證技術,它透過多次隨機抽樣將資料集切割成訓練集與測試集。每次隨機分割時,測試集的比例固定,而訓練集和測試集則隨機選取。這種方法主要透過多次隨機測試不同的資料切分方式來評估模型性能,最後取測試結果的平均值。

+

優點:

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    +
  1. 多次隨機抽樣能減少資料劃分的偏差。
  2. +
  3. 可以靈活選擇訓練和測試集的比例。
  4. +
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缺點:

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  1. 多次重複抽樣需要較大的計算資源。
  2. +
  3. 每次隨機抽樣的資料集可能會有所不同,結果不穩定。
  4. +

Bootstrapping

還有一種比較特殊的交叉驗證方式,Bootstrapping 自助抽樣法。是一種從給定訓練集中有放回的均勻抽樣,也就是說,每當選中一個樣本,它等可能地被再次選中並被再次添加到訓練集中。假設每次訓練都採樣十個樣本,在這十筆資料中很有可能會再次被隨機抽到。剩下沒有抽到的資料則都變成測試集,用來評估訓練完的模型。

+

優點:

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    +
  1. 能在小數據集的情況下提高模型穩定性。
  2. +
  3. 可重複利用相同的數據來進行多次訓練。
  4. +
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缺點:

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    +
  1. 重複樣本可能導致模型過擬合。
  2. +
  3. 測試集中資料量較少,可能導致模型泛化能力評估不夠充分。
  4. +

小結

交叉驗證是訓練模型中非常重要的技巧,尤其是當手邊的資料集有限時更應該使用。透過交叉驗證技巧,即使在數據有限的情況下,我們也能夠獲得準確的結果,並且可以避免模型過度擬合。並為我們提供更準確的模型預測性能估計方式,同時也能夠提升模型的泛化能力。以上的方法可以直接使用 scikit-learn 裡面 model_selection 底下的 cross_val_score 方法進行實作。

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zn1(_9p>Lvr_JLx^bkJz(o=^5OhJb?B9c9d==Oy1;ED~M&yFbczK+a*JDK_M+Umkdl z`o)4QAU=LzIJF!#&Tf`BwZpfbK~vDor_)M#7kD$8Y3yggh_(`R&&>sst~#f4vI60J zWHjKgQ|KTn1|H*l(F1s?ek-sn7 zi^Nuzx__Y0u$2h0{V(Yk9-RAFo5}M{Lc4-xo_D3223axqMUhp zaz~sx(AKckYu)-&v;Te3Rg9;Qtp;>#b&6f#(22}OtY1*o4H{kujC)93>0vll0 zZkfXXWg_O$Uio{({~w<^?9@&6kFuph9aE9X9|@07l$w<+6(YRZo9EK%Lba$b4m|Xy z6DB+^w%GW;J<&hE`C9O)K+^QlZskx*D1&HLE89arnWY2d+}84ZfoaoKJ^R(7!6y{{ zcYu@xK^*`7K>xn9&(ypqVAf2d!RLqR@<0POFCoPjn1>;xZ1T6mAnraOpkI2OTYtHg zOjV~&Q$GPley+x%dfnn3>;1!RKmtydr#GJ~-Szw1_@v~7hB4s&T7dpL9hp@8yU6+P z3;ElB)0KJS{IDs1AD493-|cg4kJqJHAcC1;0+JN7=Eo%w|ASbNN=V%KM^D190s*wY zza@!=H|Z6yT~2^!q0)F3_}6zjNR!360vbm~Q$|gYMY6H(0n63A1Ss)*uGQH8u--o; zJM$b6HacV~;&(&-KRn(amz#eBE|2$i6;Ap~4gTkTpDZ9b637U~ek2wD?=BZk1TKGD z7-jvt!1KpX|A&tOr6<&EE>DTyO!EJ5%^%D8zYl8qr=|Z*=uT3=7Qj^;+y8nC{uJ^b zf%gCEhF-vm51%i6*Z&J|^IyIs?k=Fy-}E|4{#vL1+`GmxK=sn->NtMS#P~}m{5L1k z`1XVcK*Z@w{nfGZ@3Mt*0gj_IL)z+pce(!clPKAYyZhe<b-T oA3pwnrT(D102w5?p#T5? diff --git "a/26.\344\272\244\345\217\211\351\251\227\350\255\211 K-Fold Cross-Validation/index.html" "b/26.\344\272\244\345\217\211\351\251\227\350\255\211 K-Fold Cross-Validation/index.html" index d745763..973179d 100644 --- "a/26.\344\272\244\345\217\211\351\251\227\350\255\211 K-Fold Cross-Validation/index.html" +++ "b/26.\344\272\244\345\217\211\351\251\227\350\255\211 K-Fold Cross-Validation/index.html" @@ -91,7 +91,7 @@
- +
@@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
- +
@@ -315,8 +315,8 @@
  • - - K-Fold Cross-Validation + + K-Fold Cross Validation
  • @@ -325,18 +325,18 @@
  • - - Repeated K-Fold + + Repeated K-Fold Cross Validation
  • - - Stratified K-Fold + + Stratified K-Fold Cross Validation
  • - - Group K-Fold + + Group K-Fold Cross Validation
  • @@ -387,8 +387,8 @@
  • - - K-Fold Cross-Validation + + K-Fold Cross Validation
  • @@ -397,18 +397,18 @@
  • - - Repeated K-Fold + + Repeated K-Fold Cross Validation
  • - - Stratified K-Fold + + Stratified K-Fold Cross Validation
  • - - Group K-Fold + + Group K-Fold Cross Validation
  • @@ -422,17 +422,17 @@

    [Day 26] 交叉驗證 K-Fold Cross-Valid

    今日學習目標

    • 了解 K-Fold 各種不同變形
        -
      • K-Fold Cross-Validation
      • +
      • K-Fold Cross Validation
      • Nested K-Fold Cross Validation
      • -
      • Repeated K-Fold
      • -
      • Stratified K-Fold
      • -
      • Group K-Fold
      • +
      • Repeated K-Fold Cross Validation
      • +
      • Stratified K-Fold Cross Validation
      • +
      • Group K-Fold Cross Validation

    前言

    交叉驗證又稱為樣本外測試,是資料科學中重要的一環。透過資料間的重複採樣過程,用於評估機器學習模型並驗證模型對獨立測試數據集的泛化能力。在今天的文章中我們將詳細的來介紹每一種 K-Fold 變型。

    -

    K-Fold Cross-Validation

    +

    K-Fold Cross Validation

    在 K-Fold 的方法中我們會將資料切分為 K 等份,K 是由我們自由調控的,以下圖為例:假設我們設定 K=10,也就是將訓練集切割為十等份。這意味著相同的模型要訓練十次,每一次的訓練都會從這十等份挑選其中九等份作為訓練資料,剩下一等份未參與訓練並作為驗證集。因此訓練十回將會有十個不同驗證集的 Error,這個 Error 通常我們會稱作 loss 也就是模型評估方式。模型評估方式有很多種,以回歸問題來說就有 MSE、MAE、RMSE...等。最終把這十次的 loss 加總起來取平均就可以當成最終結果。透過這種方式,不同分組訓練的結果進行平均來減少方差,因此模型的性能對數據的劃分就不會那麼敏感。

    參考

    @@ -446,26 +446,32 @@

    Nested K-Fold Cross Validation

    -

    Repeated K-Fold

    +

    Repeated K-Fold Cross Validation

    另一個 K-Fold 變型為 Repeated K-Fold 顧名思義就是重複 n 次 K-Fold cross-validation。假設 K=2、n=2 代表 2-fold cross validation,在每一回合又會將資料將會打亂得到新組合。因此最終會得到 4 組的資料,意味著模型將訓練四遍。此種方法會確保每次組合的隨機資料並不會重複。簡單來說執行 K-Fold 交叉驗證,然後重新洗牌數據,然後再次執行 K-Fold。

    -

    Stratified K-Fold

    +

    Stratified K-Fold Cross Validation

    分層交叉驗證,每個 Fold 都是按照類別的比例抽出來的。假設這個分類任務一共有三個類別A、B、C,它們的比例是1:4:8。那麼每個fold中的A、B、C的比例也必須是1:4:8。其實現方式也非常簡單,首先依序把A、B、C類別的數據隨機分成k組,最後再把它們合併依照比例起來,就得到了k組滿足1:2:10的數據了。

    -

    優點: -1. 優於一般的 K-Fold 因為test set能充分代表整體數據。 -2. 預測結果的方差也會變小,使得交叉驗證的 error 更可靠。 -3. 對於資料不平衡的數據很有用

    -

    缺點: -1. 大多實例都以分類問題為主

    - -

    Group K-Fold

    + +

    Group K-Fold Cross Validation

    此做法為了避免取連續的資料而造成測試集或驗證集偏向某一特別的狀況而造成過度擬和訓練集,反而在未看過的資料下表現不好。Group K-Fold 為了避免此情況發生,它切割資料時有效的從資料集中每個區塊隨機挑選作為驗證集。同時保證每一個 Fold 的驗證集並不會重複的資料。假設你有三個類別,至少驗證集必須從三個不同的分組中抽樣取出,同時確保每一個 Fold 所抽出來的這三個分組並不會重複。

      diff --git "a/27.\346\251\237\345\231\250\345\255\270\347\277\222\345\270\270\347\212\257\351\214\257\347\232\204\345\215\201\344\273\266\344\272\213/index.html" "b/27.\346\251\237\345\231\250\345\255\270\347\277\222\345\270\270\347\212\257\351\214\257\347\232\204\345\215\201\344\273\266\344\272\213/index.html" index 68ee448..3037a74 100644 --- "a/27.\346\251\237\345\231\250\345\255\270\347\277\222\345\270\270\347\212\257\351\214\257\347\232\204\345\215\201\344\273\266\344\272\213/index.html" +++ "b/27.\346\251\237\345\231\250\345\255\270\347\277\222\345\270\270\347\212\257\351\214\257\347\232\204\345\215\201\344\273\266\344\272\213/index.html" @@ -91,7 +91,7 @@
    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
    @@ -505,7 +505,8 @@

    1. 資料收集與處理不當

    2. 訓練集與測試集的類別分佈不一致

    在分類的資料中,初學者常見的錯誤是忘記使用分層抽樣 (stratify) 來對訓練集和測試集進行切割。當測試集的分佈盡可能與訓練相同情況下,模型才更有可能得到更準確的預測。然而在分類的問題中,我們必須更關注每個類別的資料分佈比例。以下舉個例子:假設我們有三個標籤的類別,而這三個類別的分佈比例分別為 4:3:3。同理我們在進行資料切割的時候必須確保訓練集與測試集需要有相同的資料分佈比例。

    大家應該都使用過 Sklearn 的 train_test_split 進行資料切割。在此方法中 Sklearn 提供了一個 stratify 參數達到分層隨機抽樣的目的。特別是在原始數據中樣本標籤分佈不均衡時非常有用,一些分類問題可能會在目標類的分佈中表現出很大的不平衡:例如,負樣本與正樣本比例懸殊(信用卡盜刷預測、離職員工預測)。以下用紅酒分類預測來進行示範,首先我們不使用 stratify 隨機切割資料並查看資料切割前後的三種類別比例。

    -
    from sklearn.datasets import load_wine
    +
    import pandas as pd
    +from sklearn.datasets import load_wine
     from sklearn.model_selection import train_test_split
     
     X, y = load_wine(return_X_y=True)
    @@ -607,7 +608,7 @@ 

    5. 資料處理不當導致資料洩漏

    更強大的解決方案是使用 Sklearn 內建的 pipeline,它能夠保護模型免於資料洩漏的問題。此方法能夠確保訓練資料僅參與轉換擬合與模型訓練,而測試資料僅用於計算並驗證模型。


    6. 僅使用測試集評估模型好壞

    -

    如果你的測試資料 R2 score 得到了 0.85 就代表很好了嗎?不盡然!儘管有高的測試分數通常意味著模型表現佳,但在解釋測試結果時仍有一些重要的注意事項。首先最重要的,無論分數值如何測試集的分數一定要與訓練集相比較才能確保模型訓練好與壞。當你的模型訓練集分數高於測試集的分數,並且兩者都足夠高以滿足專案的目標期望時這代表你訓練了一個好模型。然而這並不意味著訓練和測試分數之間的差異越大越好。舉個例子,若訓練集的 R2 score 為 0.85 測試集為 0.8 即代表模型既不過度擬合(overfit)也不欠擬合(underfit)。但是如果訓練集 0.9 測試集 0.8 的時候,你的模型就是過擬合。其原因是該模型沒有在訓練期間進行泛化,而是記住了一些訓練數據,從而導致測試分數低得多。

    +

    如果你的測試資料 R2 score 得到了 0.85 就代表很好了嗎?不盡然!儘管有高的測試分數通常意味著模型表現佳,但在解釋測試結果時仍有一些重要的注意事項。首先最重要的,無論分數值如何測試集的分數一定要與訓練集相比較才能確保模型訓練好與壞。當你的模型訓練集分數高於測試集的分數,並且兩者都足夠高以滿足專案的目標期望時這代表你訓練了一個好模型。然而這並不意味著訓練和測試分數之間的差異越大越好。舉個例子,若訓練集的 R2 score 為 0.85 測試集為 0.8 即代表模型既不過度擬合(overfit)也不欠擬合(underfit)。但是如果訓練集 0.9 測試集 0.7 的時候,你的模型就是過擬合。其原因是該模型沒有在訓練期間進行泛化,而是記住了一些訓練數據,從而導致測試分數低得多。

    在大多數任務中你將會看到許多人使用 tree-based 模型或是整體學習模型 (ensemble models)。例如在隨機森林演算法當中如果它們的樹深度太深,往往會獲得非常高的訓練分數,從而導致過度擬合。另外也有測試集的分數比訓練集高的情況,若發生此情況時通常都會感覺是不是做錯了什麼。這種情況的主要原因是資料洩漏,也就是上一節我們討論的情況。或是你的測試資料筆數太少,沒辦法足以驗證模型好壞。

    另外有時候我們也會得到在訓練集有很好的表現但測試集無敵差的情況。當訓練和測試分數差異很大時,問題往往與測試集有關而不是過度擬合。這時候你可能要檢查資料預處理的方式是否一致 (像是取 log 或 scale),或是只是忘記對測試集做轉換處理。

    這裡做一個小結,總之在訓練好模型時請仔細檢查訓練和測試分數之間的差距。並且可以透過此評估方式檢視模型是否過擬合,同時也能進行模型條參或是選擇最佳的資料預處理方式。並為最終的模型做最佳的準備。

    diff --git "a/28.\345\204\262\345\255\230\350\250\223\347\267\264\345\245\275\347\232\204\346\250\241\345\236\213/index.html" "b/28.\345\204\262\345\255\230\350\250\223\347\267\264\345\245\275\347\232\204\346\250\241\345\236\213/index.html" index 5441b14..0f56189 100644 --- "a/28.\345\204\262\345\255\230\350\250\223\347\267\264\345\245\275\347\232\204\346\250\241\345\236\213/index.html" +++ "b/28.\345\204\262\345\255\230\350\250\223\347\267\264\345\245\275\347\232\204\346\250\241\345\236\213/index.html" @@ -91,7 +91,7 @@
    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
    diff --git "a/29.\344\275\277\347\224\250Python-Flask\346\236\266\350\250\255API\345\220\247/index.html" "b/29.\344\275\277\347\224\250Python-Flask\346\236\266\350\250\255API\345\220\247/index.html" index aa7652e..5f1c795 100644 --- "a/29.\344\275\277\347\224\250Python-Flask\346\236\266\350\250\255API\345\220\247/index.html" +++ "b/29.\344\275\277\347\224\250Python-Flask\346\236\266\350\250\255API\345\220\247/index.html" @@ -91,7 +91,7 @@
    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
    diff --git "a/3.\344\275\240\347\234\237\344\272\206\350\247\243\350\263\207\346\226\231\345\227\216\350\251\246\350\251\246\347\234\213\350\246\226\350\246\272\345\214\226\345\210\206\346\236\220\345\220\247/index.html" "b/3.\344\275\240\347\234\237\344\272\206\350\247\243\350\263\207\346\226\231\345\227\216\350\251\246\350\251\246\347\234\213\350\246\226\350\246\272\345\214\226\345\210\206\346\236\220\345\220\247/index.html" index 4c92936..fda8dad 100644 --- "a/3.\344\275\240\347\234\237\344\272\206\350\247\243\350\263\207\346\226\231\345\227\216\350\251\246\350\251\246\347\234\213\350\246\226\350\246\272\345\214\226\345\210\206\346\236\220\345\220\247/index.html" +++ "b/3.\344\275\240\347\234\237\344\272\206\350\247\243\350\263\207\346\226\231\345\227\216\350\251\246\350\251\246\347\234\213\350\246\226\350\246\272\345\214\226\345\210\206\346\236\220\345\220\247/index.html" @@ -91,7 +91,7 @@
    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
    diff --git "a/30.\344\275\277\347\224\250 Heroku \351\203\250\347\275\262\346\251\237\345\231\250\345\255\270\347\277\222 API/index.html" "b/30.\344\275\277\347\224\250 Heroku \351\203\250\347\275\262\346\251\237\345\231\250\345\255\270\347\277\222 API/index.html" index d4a21aa..39a98c3 100644 --- "a/30.\344\275\277\347\224\250 Heroku \351\203\250\347\275\262\346\251\237\345\231\250\345\255\270\347\277\222 API/index.html" +++ "b/30.\344\275\277\347\224\250 Heroku \351\203\250\347\275\262\346\251\237\345\231\250\345\255\270\347\277\222 API/index.html" @@ -91,7 +91,7 @@
    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
    diff --git "a/4.\345\222\261\345\200\221\344\270\200\350\265\267\345\201\232\350\263\207\346\226\231\346\270\205\347\220\206\345\222\214\345\211\215\350\231\225\347\220\206/index.html" "b/4.\345\222\261\345\200\221\344\270\200\350\265\267\345\201\232\350\263\207\346\226\231\346\270\205\347\220\206\345\222\214\345\211\215\350\231\225\347\220\206/index.html" index 99420f0..7dff3e5 100644 --- "a/4.\345\222\261\345\200\221\344\270\200\350\265\267\345\201\232\350\263\207\346\226\231\346\270\205\347\220\206\345\222\214\345\211\215\350\231\225\347\220\206/index.html" +++ "b/4.\345\222\261\345\200\221\344\270\200\350\265\267\345\201\232\350\263\207\346\226\231\346\270\205\347\220\206\345\222\214\345\211\215\350\231\225\347\220\206/index.html" @@ -91,7 +91,7 @@
    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
    diff --git a/404.html b/404.html index 1e474d1..02a2bcd 100644 --- a/404.html +++ b/404.html @@ -153,7 +153,7 @@ - +
    @@ -200,7 +200,7 @@ - +
    diff --git "a/5.\346\251\237\345\231\250\345\255\270\347\277\222\345\244\247\350\243\234\345\270\226/index.html" "b/5.\346\251\237\345\231\250\345\255\270\347\277\222\345\244\247\350\243\234\345\270\226/index.html" index d10a301..dadd791 100644 --- "a/5.\346\251\237\345\231\250\345\255\270\347\277\222\345\244\247\350\243\234\345\270\226/index.html" +++ "b/5.\346\251\237\345\231\250\345\255\270\347\277\222\345\244\247\350\243\234\345\270\226/index.html" @@ -91,7 +91,7 @@
    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
    diff --git "a/6.\351\235\236\347\233\243\347\235\243\345\274\217\345\255\270\347\277\222k-means\345\210\206\347\276\244/index.html" "b/6.\351\235\236\347\233\243\347\235\243\345\274\217\345\255\270\347\277\222k-means\345\210\206\347\276\244/index.html" index bacfe6b..c66eef9 100644 --- "a/6.\351\235\236\347\233\243\347\235\243\345\274\217\345\255\270\347\277\222k-means\345\210\206\347\276\244/index.html" +++ "b/6.\351\235\236\347\233\243\347\235\243\345\274\217\345\255\270\347\277\222k-means\345\210\206\347\276\244/index.html" @@ -91,7 +91,7 @@
    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
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    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
    - +
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    - +
    @@ -120,7 +120,7 @@ 全民瘋AI系列 [經典機器學習]
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    - +
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