glm.fit: došlo k numericky pravděpodobnosti 0 nebo 1, ale funkce vinníka je číselná

Dostávám varovnou zprávu v nadpisu a zkontroloval jsem příspěvky jako jako např. tento .

Chtěl bych pochopit, jak má tato funkce dokonalé oddělení od cílové proměnné, protože jsem předpokládal, že toto druh varování by byl více spojen s kategorickými funkcemi, kde jedna konkrétní úroveň obsahuje všechny cílové třídy true nebo false.

Kontext je konverze webu (transakce způsobí nákup True = X1 or not = False X0 ). Chtěl jsem pochopit dopad průměrné doby načítání stránky pro danou relaci webu. Po odebrání dalších funkcí, jako je typ zařízení a zdroj provozu, jsem zjistil, že dostávám pouze varování s funkcí Avg_Load_Time, která je číselná (dbl) funkce.

Moje další myšlenka byla, že možná všechny ty relace s průměrnou dobou načítání 0 způsobovaly perfektní se paration nicméně nemám žádné nuly, jen některé blízké 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 

Poté jsem se podíval na souhrn průměrné doby načítání pouze pro tyto relace s transakcí, kde target je tedy 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 

Vidím zde, že zatímco min je vyšší, není to 0.

Jak Mohu najít příčinu svého dokonalého oddělení, protože jsem to zúžil na jedinou funkci?

Zde je ukázka 1000, pokud to pomůže. Jakékoli tipy na pochopení mého oddělení ocenily:

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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Upravit: Zde je úplný kód, který žádám o spuštění modelu:

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" ) 

Po spuštění se zobrazí varovná zpráva.

Komentáře

• Jaký je plný model, který se hodí? Interakce s jinými proměnnými? Jak také víte, že ' s touto funkcí způsobuje problém?
• @Glen Tuto položku jsem nyní přidal.
• Zobrazuje se vám chyba, pokud se hodí celý soubor dat bez životopisu / školení? Vypadá to jako vysoce nevyvážené třídy a zajímalo by mě, jestli mají některé foldy v menší třídě pouze 1 nebo dokonce 0. Zkoušeli jste stratifikovat výběr záhybů podle třídy, abyste zajistili, že každý záhyb má dostatek menší třídy?
• @EdM " Zkoušeli jste stratifikovat výběr záhybů folds by class to ensure that each fold has enough of the smaller class " – Jak bych to udělal?