Copy of Assignment 7.txt - library(ISLR attach(Default...

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library(ISLR) attach(Default) set.seed(1) fit.glm <- glm(default ~ income + balance, data = Default, family = "binomial") summary(fit.glm) train <- sample(dim(Default)[1], dim(Default)[1] / 2) fit.glm <- glm(default ~ income + balance, data = Default, family = "binomial", subset = train) summary(fit.glm) ##Original probs <- predict(fit.glm, newdata = Default[-train, ], type = "response") pred.glm <- rep("No", length(probs)) pred.glm[probs > 0.5] <- "Yes" mean(pred.glm != Default[-train, ]$default) #Trial 1 train <- sample(dim(Default)[1], dim(Default)[1] / 2) fit.glm <- glm(default ~ income + balance, data = Default, family = "binomial", subset = train) probs <- predict(fit.glm, newdata = Default[-train, ], type = "response") pred.glm <- rep("No", length(probs)) pred.glm[probs > 0.5] <- "Yes" mean(pred.glm != Default[-train, ]$default) #Trial 2 train <- sample(dim(Default)[1], dim(Default)[1] / 2) fit.glm <- glm(default ~ income + balance, data = Default, family = "binomial", subset = train) probs <- predict(fit.glm, newdata = Default[-train, ], type = "response") pred.glm <- rep("No", length(probs)) pred.glm[probs > 0.5] <- "Yes" mean(pred.glm != Default[-train, ]$default) #Trial 3
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  • Fall '16
  • Natural number, Default, Brent Spiner

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