glm.fit: probabilidades ajustadas numericamente 0 ou 1 ocorreram, no entanto, o recurso culpado é numérico

Recebi a mensagem de aviso no título e analisei postagens como como, por exemplo, este .

Eu gostaria de entender como esse recurso tem separação perfeita com a variável de destino, já que assumi que isso tipo de aviso estaria mais associado a recursos categóricos, em que um determinado nível tem todas as classes de destino verdadeiro ou falso.

O contexto é a conversão do site (a transação torna uma compra Verdadeira = X1 ou não = Falso X0 ). Eu queria entender o impacto do tempo médio de carregamento da página para uma determinada sessão do site. Depois de remover outros recursos, como tipo de dispositivo e origem de tráfego, descobri que só recebo o aviso com o recurso Avg_Load_Time, que é um numérico (dbl) recurso.

Meu pensamento seguinte foi que talvez todas aquelas sessões com tempo de carregamento médio de 0 estavam causando um processo perfeito paration, no entanto, não tenho zeros, apenas alguns próximos de 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 

Eu então olhei para um resumo do tempo médio de carregamento apenas para as sessões com uma transação, onde o alvo é, portanto, 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 

Posso ver aqui que, embora o mínimo seja maior, não é 0.

Como posso encontrar a causa da minha separação perfeita, considerando que reduzi-a a um único recurso?

Aqui está uma amostra de 1000, se ajudar. Quaisquer dicas sobre como entender minha separação serão apreciadas:

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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Editar: Aqui está o código completo que estou processando para executar o modelo:

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" ) 

Depois de executar isso, recebo a mensagem de aviso.

Comentários

• Qual é o completo modelo que você está encaixando? Ele está interagindo com outras variáveis? Além disso, como você sabe que ' é esse recurso que está causando o problema?
• @Glen Eu adicionei isso à postagem agora.
• Você obtém o erro se ajustar todo o conjunto de dados sem o CV / treinamento? Parece que são classes altamente desequilibradas e me pergunto se algumas dobras têm apenas 1 ou mesmo 0 na classe menor. Você tentou estratificar a seleção de dobras por classe para garantir que cada dobra tenha o suficiente da classe menor?
• @EdM " Você já tentou estratificar a seleção de dobras por classe para garantir que cada dobra tenha o suficiente da classe menor " – Como eu faria isso?