glm.fit: tilpassede sannsynligheter numerisk 0 eller 1 skjedde, men den skyldige funksjonen er numerisk

Jeg har mottatt advarselen i tittelen og har gjennomgått innlegg som som f.eks denne .

Jeg vil gjerne forstå hvordan denne funksjonen har perfekt skille med målvariabelen, siden jeg bare antok at denne slags advarsel vil være mer assosiert med kategoriske funksjoner, der et bestemt nivå har alle sanne eller falske målklasser.

Konteksten er nettstedskonvertering (transaksjon gjør et kjøp True = X1 eller ikke = False X0 ). Jeg ønsket å forstå virkningen av gjennomsnittlig sidelastetid for en gitt nettsesjon. Etter å ha fjernet andre funksjoner som enhetstype og trafikkilde, har jeg funnet ut at jeg bare mottar advarselen med funksjonen Avg_Load_Time som er en numerisk (dbl) funksjon.

Min neste tanke var at kanskje alle de øktene med 0 gjennomsnittlig lastetid forårsaket perfekt se parering men jeg har ingen nuller, bare noen nær 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 så bare på et sammendrag av Gj.sn. lastetid for de øktene med en transaksjon, hvor målet er dermed 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 se her at mens min er høyere, er det ikke 0.

Hvordan kan jeg finne årsaken til min perfekte separasjon gitt at jeg har begrenset den til en enkelt funksjon?

Her er et utvalg på 1000 hvis det hjelper. Eventuelle tips for å forstå separasjonen min verdsatt:

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: Her er den fulle koden jeg saksøker for å kjøre 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" ) 

Etter å ha kjørt dette får jeg advarselen.

Kommentarer

• Hva er hele modell du passer på? Samhandler det med andre variabler? Hvordan vet du også at ' er denne funksjonen som forårsaker problemet?
• @Glen Jeg har lagt dette til i innlegget nå.
• Får du feilen hvis du passer til hele datasettet uten CV / opplæring? Ser ut som svært ubalanserte klasser, og jeg lurer på om noen folder bare har 1 eller til og med 0 i den mindre klassen. Har du prøvd å stratifisere utvalget av bretter etter klasse for å sikre at hver brett har nok av den mindre klassen?
• @EdM " Har du prøvd å stratifisere utvalget av bretter etter klasse for å sikre at hver brett har nok av den mindre klassen " – Hvordan ville jeg gjort det?