glm.fit: probabilità adattate numericamente 0 o 1 si è verificato tuttavia la caratteristica colpevole è numerica

Ho “ricevuto il messaggio di avviso nel titolo e ho esaminato post come come ad esempio questo .

Vorrei capire in che modo questa funzione ha una separazione perfetta con la variabile di destinazione, dal momento che ho supposto che questa tipo di avviso sarebbe più associato a caratteristiche categoriali, in cui un particolare livello ha tutte le classi di destinazione vero o falso.

Il contesto è la conversione del sito Web (la transazione effettua un acquisto Vero = X1 o no = Falso X0 ). Volevo capire limpatto del tempo medio di caricamento della pagina per una determinata sessione del sito web. Dopo aver rimosso altre funzionalità come il tipo di dispositivo e la sorgente di traffico, ho scoperto che ricevo lavviso solo con la funzione Avg_Load_Time che è un numero (dbl) caratteristica.

Il mio pensiero successivo è stato che forse tutte quelle sessioni con tempo di caricamento medio pari a 0 stavano causando un se perfetto paration tuttavia non ho zeri, solo alcuni vicini a 0:

> 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 

Ho quindi esaminato un riepilogo del tempo di caricamento medio solo per quelle sessioni con una transazione, dove il target è quindi X1:

> 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 

Qui posso vedere che mentre il min è più alto, non è 0.

Come posso trovare la causa della mia separazione perfetta, dato che lho ristretta a una singola funzione?

Ecco un esempio di 1000 se aiuta. Apprezzato qualsiasi suggerimento per comprendere la mia separazione:

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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Modifica: ecco il codice completo che sto facendo causa per lanciare il modello:

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" ) 

Dopo aver eseguito questo, ricevo il messaggio di avviso.

Commenti

• Qual è il messaggio completo modello ti stai adattando? Interagisce con altre variabili? Inoltre, come fai a sapere che ' è questa caratteristica che causa il problema?
• @Glen Lho aggiunto al post adesso.
• Ricevi lerrore se inserisci lintero set di dati senza il CV / la formazione? Sembrano classi altamente sbilanciate e mi chiedo se alcune pieghe hanno solo 1 o anche 0 nella classe più piccola. Hai provato a stratificare la selezione di pieghe per classe per assicurarti che ciascuna piega abbia abbastanza della classe più piccola?
• @EdM " Hai provato a stratificare la selezione di piega per classe per garantire che ogni piega abbia abbastanza della classe più piccola " – Come potrei farlo?