glm.fit: les probabilités ajustées numériquement 0 ou 1 se sont produites, mais la caractéristique coupable est numérique

Jai reçu le message davertissement dans le titre et jai examiné les messages tels comme par exemple celui-ci .

Je voudrais comprendre comment cette fonctionnalité a une séparation parfaite avec la variable cible, car je viens de supposer que cela type davertissement serait plus associé à des fonctionnalités catégorielles, où un niveau particulier a toutes les classes cibles vraies ou fausses.

Le contexte est la conversion du site Web (la transaction effectue un achat Vrai = X1 ou non = Faux X0 ). Je voulais comprendre limpact du temps de chargement moyen des pages pour une session de site Web donnée. Après avoir supprimé dautres fonctionnalités telles que le type dappareil et la source de trafic, jai constaté que je ne reçois lavertissement quavec la fonctionnalité Avg_Load_Time qui est une valeur numérique (dbl) .

Ma prochaine pensée était que peut-être toutes ces sessions avec un temps de chargement moyen de 0 provoquaient paration cependant je nai pas de zéros, juste quelques-uns proches 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 

Jai ensuite regardé un résumé du temps de chargement moyen uniquement pour les sessions avec une transaction, où la cible est donc 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 

Je peux voir ici que si le min est plus élevé, ce nest pas 0.

Comment puis-je trouver la cause de ma séparation parfaite étant donné que je « lai réduit à une seule fonctionnalité?

Voici un échantillon de 1000 si cela aide. Tous les conseils pour comprendre ma séparation appréciés:

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, 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, 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, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 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, 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, 2L, 2L, 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, 2L, 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, 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, 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, 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, 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, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L, 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), .Label = c("X0", "X1"), class = "factor"), Avg_Load_Time = c(0.77, 39.1, 5.34, 5.45, 1.74, 2.18, 9.19, 4.73, 9.37, 2.45, 4.33, 1.86, 1.93, 4.32, 18.13, 6.93, 3.57, 13.93, 130.38, 4.47, 26.67, 14.48, 19.54, 9.41, 6.51, 3.78, 1.91, 2.98, 5.47, 2.24, 3.07, 27.9, 8.8, 65.66, 10.23, 3.32, 1.81, 5.02, 2.71, 1.04, 11.76, 5.73, 2.32, 3.54, 2.3, 63.9, 4.5, 0.78, 1.44, 4.06, 0.7, 1.79, 7.7, 4.3, 33.25, 1.44, 0.79, 6.39, 4.17, 0.6, 3.58, 16.84, 11.07, 16.05, 28.29, 9.22, 4.1, 7.81, 0.55, 64.88, 3.32, 10.44, 3.22, 1.57, 1.01, 7.16, 3.41, 5.74, 3.73, 2.62, 4.39, 17.92, 5.05, 1.94, 6.95, 1.86, 27.07, 7.69, 4.05, 2.96, 8.03, 3.21, 5.33, 1.62, 17.03, 8.37, 1.7, 5.08, 4.96, 0.83, 4.65, 16.36, 7.04, 4.9, 22.98, 6.08, 4.3, 2.91, 1.52, 1.81, 11.28, 16.71, 4.17, 9.62, 3.18, 2.66, 0.78, 9.3, 25.39, 5.84, 1.13, 58.03, 1.45, 10.45, 19.5, 1.25, 1.06, 30.49, 2.9, 7.31, 3.61, 4.64, 0.68, 10.43, 8.84, 1.78, 17.16, 6.68, 4.61, 7.43, 5.03, 2.98, 2.89, 4.15, 9.47, 3.68, 2.16, 2.09, 41.78, 3.06, 113.4, 30.13, 5.37, 14.83, 2.1, 2.03, 13.51, 3.1, 5.54, 4.61, 18.09, 23.82, 34.64, 4.99, 8.35, 7.45, 3.98, 3.44, 1.01, 34.45, 64.03, 2.82, 13.63, 13.34, 0.66, 4.15, 2.06, 19.7, 1.38, 2.16, 10.65, 5.89, 57.27, 17.51, 3.5, 10.97, 2.2, 9.38, 2.06, 5.25, 4.11, 72.22, 0.93, 3.65, 5.71, 4.79, 3.01, 0.95, 6.6, 15.35, 1.05, 3.31, 3.44, 8.31, 11.35, 6.63, 4.87, 4.83, 10.05, 1.01, 25.35, 3.79, 11.14, 24.26, 9.71, 1.76, 3.75, 1.66, 7.02, 6.41, 3.72, 3.58, 35.16, 3.24, 2.29, 9.61, 9.31, 0.67, 0.63, 7.08, 10.85, 2.65, 4.35, 5.86, 3.24, 4.32, 3.34, 2.37, 4.23, 1.97, 1.83, 15.42, 4.17, 5.18, 2.37, 8.91, 0.71, 20.18, 5.96, 1.41, 3.11, 26.85, 2.47, 5.99, 2.53, 1.86, 2.67, 13.66, 8.28, 5.7, 8.1, 3.95, 139.35, 15.37, 2.55, 2.85, 5.46, 2.55, 17.16, 2.87, 23.42, 1.58, 62.58, 7.5, 14.41, 1.57, 4.42, 5.41, 4.62, 12.5, 3.3, 4.37, 3.91, 3.35, 7.27, 1.11, 24.86, 18, 8.83, 7.87, 2.68, 2.77, 32.58, 12.66, 2.64, 9.89, 30.86, 10.17, 3.49, 37.99, 4.99, 12.98, 1.75, 11.92, 45.36, 3.35, 2.28, 2.83, 19.92, 9.33, 4.98, 19.76, 2.92, 3.84, 4.8, 205.98, 4.53, 8.82, 3.74, 21.8, 3.56, 3.9, 2.29, 7.85, 79.96, 3.56, 2.78, 5.9, 2.93, 3.76, 1.79, 12.94, 2.34, 25.17, 22.71, 4.15, 6.87, 147.62, 6.1, 3.23, 93.41, 12.91, 4.93, 3.22, 5.84, 8.73, 17.73, 79.63, 182.45, 2.36, 1.62, 1.22, 1.09, 3.75, 0.93, 1.82, 12.14, 4.38, 2.1, 0.88, 4.36, 1.33, 3.74, 2.85, 2.34, 13.2, 5.44, 9.94, 6.6, 2.79, 7.7, 10.99, 11.43, 19.7, 3.79, 2.26, 1.68, 23.24, 7.41, 3.13, 5.22, 2.4, 4.48, 2.35, 10.36, 1.25, 34.14, 7.37, 3.46, 18.84, 8.32, 4.9, 2.37, 1.03, 4.56, 9.7, 20.95, 1.01, 17.42, 9.29, 0.88, 3.84, 13.82, 0.52, 4.51, 11.74, 1, 6.28, 5.49, 6.13, 5.62, 0.53, 6.72, 2.08, 3.38, 68.72, 4.56, 2.45, 15.21, 5.54, 5.13, 3.86, 4.89, 1.21, 3.88, 4.83, 4.97, 8.22, 5.76, 4.07, 6.83, 1.94, 120.71, 3.26, 7.38, 4.21, 5.95, 3.7, 1.28, 3.43, 1.42, 1.63, 3.97, 10.57, 8.98, 2.37, 21.73, 8.04, 5.18, 2.48, 5.74, 4.65, 1.85, 6.75, 0.98, 1.72, 4, 6.08, 7.21, 8, 10.98, 1.94, 0.75, 30.3, 7.29, 3.31, 4.3, 66.62, 3.87, 3.01, 1.56, 3.37, 5.44, 6.76, 6.21, 1.39, 8.02, 2.95, 9.56, 1.62, 2.28, 0.46, 2, 12.55, 4.66, 15.48, 1.76, 5.81, 1.94, 4.25, 2.65, 1.51, 2.7, 27.43, 46.24, 2.67, 16.77, 0.7, 0.4, 6.07, 11.3, 1.49, 3.45, 3.2, 22.74, 1.5, 0.7, 2.6, 7.89, 2.57, 3.42, 2.46, 1.7, 2.45, 2.12, 7.97, 9.4, 3.58, 7.2, 12.18, 15.27, 2.94, 5.19, 7.33, 7.54, 5.01, 5.08, 10.65, 16.13, 2.46, 5.28, 3.02, 2.82, 10.84, 0.53, 4.22, 3.51, 10.69, 4.31, 2.55, 7.58, 19.3, 4.97, 9.39, 1.66, 0.45, 2.71, 0.82, 0.7, 8.76, 21.98, 1.95, 1.09, 3.78, 2.71, 2.55, 1.69, 17.2, 6.37, 11.42, 2.33, 0.98, 52.6, 1.67, 1.32, 21.99, 34.11, 4.99, 4.52, 6.84, 2.45, 0.7, 1.16, 9.52, 21.73, 2.32, 5.26, 7.34, 3.55, 2.6, 4.29, 9.48, 0.48, 7.22, 1.94, 4.25, 6.62, 6.76, 3.39, 1.67, 3.81, 38.39, 3.49, 65.29, 3.59, 11.54, 1.87, 4.21, 6.6, 7.3, 8.97, 9.82, 2.65, 4.99, 2.03, 4.81, 3.08, 6.41, 1.29, 1.04, 3.53, 1.29, 4.07, 2.92, 2.91, 3.82, 4.94, 2.25, 10.05, 8.87, 1.51, 3.26, 3.4, 0.68, 7.64, 0.6, 0.78, 6.25, 2.89, 17.56, 4.83, 5.55, 9.6, 3.31, 2.43, 6.96, 5.05, 5.95, 6.96, 15.06, 45.99, 1.74, 3.48, 1.83, 2.76, 6.35, 24.95, 1.96, 2.23, 2.23, 17.25, 5.2, 12.57, 11.58, 10.85, 2.91, 1.1, 3.2, 6.4, 3.15, 5.55, 1.72, 2.34, 1.83, 49.76, 1.87, 5.72, 3.59, 0.81, 8.8, 6.76, 2.06, 3.15, 9.06, 15.15, 1.64, 4.92, 9.64, 3.7, 1.78, 1.88, 3.98, 4.93, 3.37, 10.57, 4.41, 4.67, 6.39, 3.51, 21.83, 2.33, 0.68, 1.66, 2.89, 4.57, 360.7, 5.89, 6.63, 8.59, 0.48, 8.08, 2.01, 1.59, 12.45, 0.99, 2.3, 2.79, 1.47, 2.78, 2.05, 3.12, 17.84, 185.53, 3.71, 0.8, 1.82, 12.42, 31.16, 2.27, 19.23, 1.48, 7.22, 0.24, 11.73, 1.25, 14.06, 11.55, 1.48, 1.73, 5.01, 1.66, 2.25, 3.26, 6.73, 4.66, 1.8, 5.25, 8.15, 3.94, 2.72, 1.69, 25.96, 4.46, 1.51, 1.61, 1.67, 2.16, 5.24, 22.86, 3.64, 10.68, 4.65, 0.62, 0.64, 7.69, 3.63, 37.52, 9.98, 3.27, 10.94, 1.92, 2.4, 1.04, 6.05, 5.34, 3.4, 4.08, 72.08, 3.95, 5.1, 1.44, 17.06, 2.14, 4.17, 3.39, 7.79, 5.71, 19.87, 2.54, 2.49, 3.44, 3.85, 12.06, 12.18, 1.7, 3.12, 17.3, 4.41, 4.4, 0.82, 57.91, 124.91, 5.35, 5.41, 20.75, 13.54, 0.82, 0.84, 8.62, 10.04, 1.08, 10.49, 7.05, 2.72, 1.18, 2.05, 6.87, 3.51, 20.66, 4.69, 31.9, 4.64, 6.04, 1.71, 6.91, 70.11, 2.83, 9.88, 2, 10.48, 4.25, 12.24, 1.27, 50.22, 0.85, 3.51, 5.47, 0.69, 1.45, 2.97, 1.58, 2.2, 6.79, 15.88, 3.52, 1.75, 18.68, 3.81, 2.87, 4.06, 69.44, 91.15, 0.79, 1.15, 6.57, 1.18, 4.33, 7.3, 42.46, 40.83, 6.48, 32.34, 3.16, 41.11, 4.61, 1.57, 2.22, 1.2, 2.35, 10.48, 6.82, 5.38, 5.51, 3.34, 57.3, 51.9, 10.52, 1.85, 3.37, 4.42, 1.09, 29.53, 1.76, 2.48, 2.54, 10.22, 11.62, 59.79, 176.17, 7.18, 4.36, 1.76, 7.34, 4.55, 8.21, 3.94, 9.64, 1.62, 19.5, 5.53, 5.28, 1.59, 43.85, 24.02, 5.95, 6.34, 4.54, 3.71, 1.48, 9.18, 5.56, 6.08, 15.67, 24.48, 0.8, 12.53, 4.14, 29.11, 19.85, 2.54, 92.42, 44.65, 8.07, 2.44, 3.93, 3.79, 13.65, 17.64, 3.67, 9.42, 3.43, 1.81, 11.76, 1.63, 4.27, 5.87, 11.66, 3.77, 1.62, 3.58, 15.66, 4.46, 8.12, 7.35, 8.62, 6.24, 4.28, 1.68, 3.93, 3.27, 2.67, 2.93, 161.22, 3.54, 2.62, 40.6, 1.09, 2.3, 9.57, 1.1, 3.33, 17.41, 7.63, 4.01, 16.9, 3.8, 2.8, 3.56, 2.51, 6.26, 1.84, 2.98, 4.92, 2.12, 6.35, 11.74, 2.64, 14.35, 452.01, 1.7, 1.91, 4.79, 2.49, 7.61, 1.54, 8.19, 7.95, 2.81, 7.08, 9.06, 5.17, 2.08, 7.92, 4.39, 22.12, 3.42, 3.82, 3.17, 17.41, 3.29, 10.66, 31.54, 3.62, 26.38, 3.43, 10.32, 1.32, 10.71, 2.75, 0.95)), row.names = c(6184L, 2551L, 2196L, 1039L, 2202L, 2513L, 6486L, 916L, 4414L, 2131L, 4485L, 48L, 4451L, 428L, 82L, 2537L, 3385L, 862L, 1963L, 4647L, 5071L, 2291L, 2995L, 3809L, 2285L, 1515L, 327L, 3483L, 65L, 3061L, 3869L, 3477L, 3101L, 2373L, 2719L, 3135L, 4565L, 1753L, 3063L, 6430L, 6003L, 2311L, 4421L, 1644L, 4624L, 3624L, 5539L, 5660L, 6346L, 2726L, 1827L, 4540L, 1783L, 6390L, 3L, 5930L, 4033L, 389L, 4441L, 4337L, 5426L, 4693L, 1528L, 1651L, 1031L, 6197L, 1658L, 1607L, 3984L, 169L, 5577L, 3275L, 4969L, 2540L, 4156L, 6473L, 5848L, 3533L, 3060L, 3899L, 1891L, 4948L, 6339L, 3585L, 720L, 4000L, 1086L, 145L, 1657L, 3040L, 3259L, 201L, 6284L, 40L, 4519L, 3823L, 3223L, 5009L, 5800L, 5318L, 6275L, 1786L, 2839L, 6337L, 1608L, 209L, 5153L, 6367L, 4579L, 354L, 4555L, 5648L, 4864L, 5039L, 1677L, 6116L, 5098L, 1642L, 4770L, 2200L, 6191L, 3071L, 450L, 3636L, 4081L, 2510L, 5294L, 1727L, 2803L, 2432L, 1601L, 3750L, 1342L, 1631L, 4963L, 5250L, 1706L, 4321L, 2363L, 5493L, 1785L, 1871L, 4915L, 3863L, 2609L, 3569L, 5090L, 6215L, 776L, 5994L, 3678L, 2258L, 2520L, 5860L, 4978L, 571L, 1565L, 4433L, 2162L, 4047L, 4313L, 6357L, 4122L, 5517L, 6401L, 709L, 2926L, 3962L, 5218L, 3417L, 4282L, 6511L, 4401L, 308L, 6254L, 2895L, 1322L, 3314L, 1255L, 3496L, 2530L, 1512L, 2848L, 4397L, 6493L, 4089L, 2933L, 3121L, 5843L, 4478L, 2383L, 799L, 3954L, 1881L, 6246L, 6538L, 5655L, 3924L, 6358L, 598L, 6321L, 2812L, 1495L, 2279L, 1566L, 1571L, 3243L, 3463L, 3446L, 4494L, 5554L, 2408L, 3205L, 1415L, 503L, 4475L, 2991L, 6206L, 3917L, 3783L, 579L, 4765L, 5490L, 2332L, 3855L, 334L, 279L, 4344L, 2040L, 3374L, 5118L, 5522L, 943L, 1384L, 4601L, 4265L, 1661L, 4688L, 4689L, 4901L, 5189L, 3486L, 5768L, 2838L, 1224L, 5894L, 797L, 64L, 5550L, 71L, 4872L, 3641L, 4625L, 3234L, 4074L, 4193L, 4694L, 4910L, 6064L, 711L, 5573L, 2679L, 435L, 3532L, 1943L, 5559L, 3315L, 3558L, 1329L, 3639L, 1315L, 3333L, 1385L, 969L, 4171L, 4913L, 6416L, 3509L, 1493L, 3441L, 4746L, 5616L, 4951L, 3169L, 4749L, 831L, 2960L, 1296L, 16L, 2343L, 1135L, 3011L, 1561L, 2271L, 6274L, 174L, 3444L, 6017L, 3905L, 2256L, 6176L, 2010L, 4810L, 390L, 1249L, 2519L, 5377L, 6018L, 5639L, 5085L, 2620L, 5812L, 4687L, 1585L, 1728L, 2769L, 3270L, 4024L, 4315L, 423L, 1338L, 2607L, 4817L, 2097L, 870L, 6315L, 904L, 2440L, 4453L, 361L, 57L, 499L, 592L, 261L, 2635L, 2813L, 529L, 2855L, 5575L, 2611L, 577L, 2758L, 4659L, 3844L, 460L, 5323L, 1192L, 2380L, 272L, 381L, 4215L, 1872L, 5269L, 4364L, 897L, 5692L, 147L, 1357L, 5217L, 5735L, 300L, 6237L, 2495L, 105L, 446L, 2340L, 998L, 4142L, 612L, 6281L, 1582L, 1222L, 1890L, 166L, 1640L, 5590L, 58L, 3018L, 142L, 3891L, 3186L, 4745L, 299L, 4523L, 5641L, 784L, 1204L, 1686L, 1584L, 3400L, 2020L, 1845L, 1339L, 2362L, 3775L, 4993L, 3140L, 6136L, 3744L, 3660L, 4153L, 2724L, 2882L, 606L, 4553L, 2163L, 1866L, 6542L, 3836L, 439L, 1593L, 4147L, 1863L, 1478L, 1836L, 5330L, 2317L, 6407L, 4020L, 6340L, 5530L, 4834L, 4014L, 5586L, 6277L, 1131L, 4902L, 1407L, 5960L, 6548L, 5643L, 4351L, 905L, 4831L, 1502L, 619L, 4279L, 6394L, 128L, 2750L, 933L, 2526L, 4238L, 3399L, 659L, 1480L, 2368L, 2682L, 5147L, 6000L, 416L, 1817L, 5850L, 2734L, 4140L, 6131L, 6076L, 5482L, 5680L, 2259L, 2351L, 4757L, 4151L, 289L, 859L, 5292L, 5635L, 1138L, 3254L, 798L, 2505L, 4556L, 1551L, 3940L, 4871L, 5242L, 418L, 6498L, 260L, 5817L, 4388L, 4007L, 3834L, 5505L, 5628L, 6338L, 761L, 5450L, 5683L, 285L, 6111L, 5526L, 3037L, 4L, 2593L, 3748L, 1503L, 4305L, 3995L, 2808L, 5340L, 723L, 5026L, 3815L, 780L, 5079L, 4068L, 819L, 5578L, 5309L, 5343L, 4748L, 5907L, 6230L, 750L, 4398L, 1132L, 608L, 6299L, 42L, 5876L, 3563L, 2357L, 4928L, 4651L, 3820L, 6556L, 2657L, 1072L, 6177L, 5854L, 1055L, 3019L, 3226L, 1947L, 2649L, 2658L, 3980L, 4411L, 4809L, 5374L, 6171L, 2297L, 4886L, 1136L, 3304L, 5831L, 6033L, 3996L, 5566L, 2274L, 5844L, 4357L, 4184L, 3931L, 1742L, 1906L, 584L, 1180L, 5983L, 2034L, 3948L, 2299L, 1073L, 4888L, 2482L, 5282L, 1443L, 2127L, 4934L, 4823L, 5775L, 1885L, 1196L, 148L, 6078L, 6388L, 6283L, 6387L, 4507L, 2845L, 6058L, 3802L, 6417L, 6221L, 2099L, 5433L, 2409L, 4856L, 4206L, 6222L, 2927L, 2702L, 456L, 4939L, 4571L, 5468L, 5040L, 2424L, 5272L, 6453L, 5051L, 4724L, 5896L, 2916L, 1310L, 5210L, 5510L, 646L, 5657L, 814L, 6170L, 676L, 6462L, 5444L, 1140L, 5464L, 5277L, 845L, 4103L, 6037L, 3394L, 5133L, 4308L, 6330L, 3808L, 3992L, 5485L, 3267L, 2779L, 1673L, 3759L, 540L, 63L, 3328L, 5014L, 6502L, 1702L, 183L, 2793L, 1387L, 1509L, 1104L, 6117L, 2521L, 1616L, 1915L, 5086L, 2052L, 980L, 1808L, 3238L, 1065L, 3380L, 5700L, 627L, 5914L, 2915L, 3048L, 3623L, 1123L, 6095L, 1816L, 5820L, 4345L, 834L, 4729L, 4228L, 4196L, 4470L, 1279L, 5591L, 1570L, 2116L, 4849L, 4395L, 226L, 476L, 1626L, 5747L, 3529L, 2431L, 1781L, 6031L, 2284L, 3319L, 1572L, 258L, 3268L, 3450L, 1602L, 6434L, 5241L, 3211L, 1457L, 973L, 5836L, 4221L, 5546L, 511L, 1494L, 4660L, 4740L, 6022L, 3065L, 4671L, 1235L, 4859L, 5285L, 6085L, 1835L, 246L, 3957L, 2888L, 6273L, 4354L, 6334L, 1819L, 5608L, 5737L, 2086L, 1058L, 2646L, 816L, 4892L, 962L, 6487L, 2038L, 4419L, 5027L, 1894L, 3495L, 587L, 3206L, 2829L, 4782L, 3643L, 1092L, 4123L, 5749L, 2676L, 2893L, 3014L, 38L, 1912L, 5211L, 2243L, 4058L, 1213L, 2605L, 2442L, 1232L, 5918L, 4185L, 3302L, 1337L, 6362L, 5555L, 307L, 2301L, 2233L, 937L, 3907L, 5225L, 5638L, 975L, 2251L, 1050L, 1491L, 6382L, 5216L, 2451L, 5973L, 5968L, 5662L, 502L, 5915L, 2422L, 4802L, 3790L, 3299L, 2436L, 2277L, 2446L, 1261L, 6100L, 3587L, 2741L, 1789L, 3988L, 2954L, 673L, 5694L, 2920L, 3473L, 578L, 5383L, 3635L, 2474L, 4929L, 2527L, 2379L, 2749L, 2919L, 4747L, 1568L, 2770L, 3580L, 4304L, 5181L, 463L, 3725L, 3582L, 6360L, 3340L, 3527L, 2487L, 5010L, 4628L, 3698L, 3776L, 1653L, 1242L, 755L, 6249L, 4548L, 4715L, 2907L, 3603L, 5111L, 3679L, 4719L, 5415L, 3942L, 3701L, 5062L, 6464L, 3886L, 4970L, 5863L, 4053L, 3203L, 2152L, 5063L, 558L, 4078L, 1168L, 3739L, 1542L, 3839L, 3160L, 6303L, 2109L, 1773L, 5431L, 2239L, 4065L, 4771L, 6126L, 478L, 1101L, 4449L, 889L, 1234L, 2784L, 1710L, 453L, 1939L, 4598L, 5976L, 3052L, 2723L, 1453L, 144L, 1011L, 347L, 2381L, 5726L, 1098L, 3801L, 2205L, 5924L, 5627L, 4158L, 1323L, 2716L, 6020L, 5811L, 2453L, 2576L, 1343L, 1320L, 599L, 4175L, 2525L, 4167L, 728L, 2376L, 3965L, 5238L, 3838L, 5333L, 6010L, 3692L, 6235L, 1547L, 6061L, 4914L, 523L, 6040L, 3971L, 5140L, 470L, 6180L, 5213L, 1000L, 5703L, 464L, 17L, 2573L, 2548L, 4077L, 6232L, 4488L, 4627L, 2826L, 5015L, 4984L, 1940L, 6304L, 1287L, 4968L, 4008L, 4960L, 6471L, 3094L, 2265L, 3780L, 5842L, 1355L, 4387L, 1961L, 3508L, 5247L, 1715L, 4510L, 2579L, 5276L, 1884L, 2056L, 572L, 4258L, 5438L, 3359L, 4644L, 2303L, 322L, 5600L, 688L, 569L, 1143L, 4504L, 1109L, 2366L, 2628L, 513L, 6001L, 3407L, 5020L, 1613L, 5690L, 5180L, 4863L, 2050L, 2599L, 2516L, 3648L, 2714L, 4472L, 5454L, 2338L, 3966L, 903L, 1241L, 2971L, 4947L, 4792L, 3717L, 3221L, 5182L, 1006L, 6137L, 2480L, 1403L, 3797L, 5872L, 4249L, 195L, 6063L, 1898L), class = "data.frame") 

Edit: Voici le code complet que je « suis en train dexécuter le modèle:

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" ) 

Après avoir exécuté ceci, jobtiens le message davertissement.

Commentaires

• Quest-ce que le plein modèle que vous montez? Interagit-il avec dautres variables? Aussi, comment savez-vous que ' est cette fonctionnalité à lorigine du problème?
• @Glen Jai ajouté ceci au message maintenant.
• Obtenez-vous lerreur si vous ajustez lensemble de données sans le CV / la formation? On dirait des classes très déséquilibrées et je me demande si certains plis nont que 1 ou même 0 dans la classe la plus petite. Avez-vous essayé de stratifier la sélection des plis par classe pour vous assurer que chaque pli en a assez de la classe la plus petite?
• @EdM " Avez-vous essayé de stratifier la sélection de se plie par classe pour sassurer que chaque pli a suffisamment de classe plus petite " – Comment puis-je faire cela?