glm.fit：近似確率0または1が発生しましたが、原因の特徴は数値です

タイトルに警告メッセージが表示され、そのような投稿を確認しましたたとえば、これ。

この機能がターゲット変数と完全に分離されていることを理解したいのですが、これはある種の警告は、1つの特定のレベルにtrueまたはfalseのいずれかのターゲットクラスがあるカテゴリ機能に関連しています。

コンテキストはWebサイトの変換です（トランザクションは購入をTrue = X1またはnot = False X0にします） ）特定のWebサイトセッションの平均ページ読み込み時間の影響を理解したかったのですが、デバイスタイプやトラフィックソースなどの他の機能を削除した後、数値（dbl）である機能Avg_Load_Timeでのみ警告が表示されることがわかりました。機能。

私の次の考えは、平均ロード時間が0のすべてのセッションが完全なseを引き起こしている可能性があるということでした。ただし、パラチオンにはゼロがなく、0に近いものがあります。

> summary(x$Avg_Load_Time) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.24 2.32 4.27 10.18 8.73 484.62 

次に、トランザクションのあるセッションのみの平均ロード時間の概要を確認しました。したがって、ターゲットはX1です。

> summary(y %>% filter(target == "X1") %>% select(Avg_Load_Time)) Avg_Load_Time Min. : 0.780 1st Qu.: 2.478 Median : 3.785 Mean : 4.253 3rd Qu.: 4.815 Max. :16.410 

最小値は高くなりますが、0ではないことがわかります。

方法完全な分離の原因を1つの機能に絞り込んだ場合、見つけることができますか？

役立つ場合は、1000のサンプルを次に示します。私の分離を理解するためのヒントはありがたいです：

dput(x %>% sample_n(1000)) structure(list(target = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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編集：モデルを実行するために私が訴えている完全なコードは次のとおりです：

library(caret) ## custom evaluation metric function my_summary <- function(data, lev = NULL, model = NULL){ a1 <- defaultSummary(data, lev, model) b1 <- twoClassSummary(data, lev, model) c1 <- prSummary(data, lev, model) out <- c(a1, b1, c1) out} ## tuning & parameters set.seed(123) train_control <- trainControl( method = "cv", number = 5, savePredictions = TRUE, verboseIter = TRUE, classProbs = TRUE, summaryFunction = my_summary ) linear_model = train( x = select(training_data, Avg_Load_Time), y = target, trControl = train_control, method = "glm", # logistic regression family = "binomial", metric = "AUC" ) 

これを実行すると、警告メッセージが表示されます。

コメント

• 内容は何ですかあなたがフィットしているモデル？他の変数と相互作用していますか？また、'この機能が問題の原因であることをどのように知っていますか？
• @Glenこれを投稿に追加しました。
• CV /トレーニングなしでデータセット全体を適合させた場合、エラーが発生しますか？非常に不均衡なクラスのように見えますが、小さいクラスではフォールドの中には1つしかないのか0つしかないのでしょうか。フォールドの選択をクラスごとに階層化して、各フォールドに十分な小さいクラスが含まれるようにしましたか？
• @EdM "の選択を階層化してみましたかクラスごとに折りたたんで、各折り目に十分な小さいクラスがあることを確認します"-どうすればよいですか？