glm.fit: passade sannolikheter numeriskt 0 eller 1 inträffade dock skyldig funktion är numerisk

Jag har fått varningsmeddelandet i titeln och har granskat inlägg som som t.ex. den här .

Jag skulle vilja förstå hur den här funktionen har perfekt separering med målvariabeln, eftersom jag bara antog att den här typ av varning skulle vara mer associerad med kategoriska funktioner, där en viss nivå har alla antingen sanna eller falska målklasser.

Kontexten är webbplatskonvertering (transaktion gör ett köp True = X1 eller inte = False X0 Jag ville förstå effekten av genomsnittlig sidladdningstid för en given webbplats-session. Efter att ha tagit bort andra funktioner som enhetstyp och trafikkälla har jag upptäckt att jag bara får varningen med funktionen Avg_Load_Time som är ett numeriskt (dbl) Funktion.

Min nästa tanke var att kanske alla de sessioner med 0 genomsnittlig laddningstid orsakade perfekt se parering men jag har inga nollor, bara några nära 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 

Jag tittade sedan på en sammanfattning av Genomsnittlig laddningstid endast för de sessioner med en transaktion, där målet är således 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 

Jag kan här se att medan min är högre är det inte 0.

Hur kan jag hitta orsaken till min perfekta separation med tanke på att jag har begränsat den till en enda funktion?

Här är ett exempel på 1000 om det hjälper. Några tips för att förstå min separation uppskattade:

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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Redigera: Här är den fullständiga koden jag stämmer för att köra modellen:

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" ) 

Efter att ha kört det här får jag ett varningsmeddelande.

Kommentarer

• Vad är hela modell du passar? Samverkar det med andra variabler? Hur vet du att ' är att den här funktionen orsakar problemet?
• @Glen Jag har lagt till detta i inlägget nu.
• Får du felet om du passar hela datamängden utan CV / utbildning? Ser ut som mycket obalanserade klasser och jag undrar om vissa veck bara har 1 eller till och med 0 i den mindre klassen. Har du försökt stratifiera valet av veck efter klass för att säkerställa att varje vik har tillräckligt med mindre klass?
• @EdM " Har du försökt stratifiera valet av vikar efter klass för att säkerställa att varje vik har tillräckligt med mindre klass " – Hur skulle jag göra det?