glm.fit: monterede sandsynligheder numerisk 0 eller 1 opstod, men den skyldige funktion er numerisk

Jeg har modtaget advarselsmeddelelsen i titlen og har gennemgået indlæg som f.eks. som f.eks denne .

Jeg vil gerne forstå, hvordan denne funktion har perfekt adskillelse med målvariablen, da jeg netop antog, at denne form for advarsel ville være mere forbundet med kategoriske funktioner, hvor et bestemt niveau har alle enten ægte eller falsk målklasse.

Konteksten er websitekonvertering (transaktion gør et køb sandt = X1 eller ikke = Falsk X0 ). Jeg ønskede at forstå virkningen af den gennemsnitlige sideindlæsningstid for en given websidesession. Efter at have fjernet andre funktioner som enhedstype og trafikkilde, har jeg fundet ud af, at jeg kun modtager advarslen med funktionen Avg_Load_Time, som er en numerisk (dbl) funktion.

Min næste tanke var, at måske alle disse sessioner med 0 gennemsnitlig belastningstid forårsagede perfekt se paration, men jeg har ingen nuller, bare nogle tæt på 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 

Jeg kiggede derefter på et resumé af gennemsnitlig belastningstid kun for de sessioner med en transaktion, hvor målet er 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 

Jeg kan her se, at mens min er højere, er det ikke 0.

Hvordan kan jeg finde årsagen til min perfekte adskillelse, da jeg har indsnævret det til en enkelt funktion?

Her er en prøve på 1000, hvis det hjælper. Eventuelle tip til forståelse af min adskillelse værdsat:

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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Rediger: Her er den fulde kode, jeg sagsøger for at køre 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 at have kørt dette, får jeg en advarselsmeddelelse.

Kommentarer

• Hvad er det fulde model, du passer til? Interagerer det med andre variabler? Hvordan ved du også, at ' er denne funktion, der forårsager problemet?
• @Glen Jeg har føjet dette til indlægget nu.
• Får du fejlen, hvis du passer til hele datasættet uden CV / træning? Ser ud som meget ubalancerede klasser, og jeg spekulerer på, om nogle folder kun har 1 eller endda 0 i den mindre klasse. Har du prøvet at stratificere udvælgelsen af folder efter klasse for at sikre, at hver fold har nok af den mindre klasse?
• @EdM " Har du prøvet at stratificere udvælgelsen af folder efter klasse for at sikre, at hver fold har nok af den mindre klasse " – Hvordan ville jeg gøre det?