This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(data.table) | |
train <- fread('train.csv'); test <- fread('test.csv') | |
# consolidate the 2 data sets after creating a variable indicating train / test | |
train$flag <- 0; test$flag <- 1 | |
dat <- rbind(train,test) | |
# change outcome, var_b and var_e into factor var | |
dat$outcome <- factor(dat$outcome) | |
dat$var_b <- factor(dat$var_b) | |
dat$var_e <- factor(dat$var_e) | |
# check the levels of var_b and var_e in this consolidated, train and test data sets | |
length(levels(dat$var_b)); length(unique(train$var_b)); length(unique(test$var_b)) | |
# get back the train and test data | |
train <- subset(dat, flag == 0); test <- subset(dat, flag == 1) | |
train$flag <- NULL; test$flag <- NULL | |
# Build Logit Model using train data and make predictions | |
logitModel <- glm(outcome ~ ., data = train, family = 'binomial') | |
preds_train <- predict(logitModel, type = 'response') | |
# Model Predictions on test data | |
preds_test <- predict(logitModel, newdata = test, type = 'response') | |
# running the above code gives us the following error: | |
# factor var_b has new levels 16060, 17300, 17980, 19060, 21420, 21820, | |
# 25220, 29340, 30300, 33260, 34100, 38340, 39660, 44300, 45460 | |
# Workaround: | |
source('remove_missing_levels.R') | |
preds_test <- predict(logitModel, | |
newdata = remove_missing_levels(fit = logitModel, test_data = test), | |
type = 'response') |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment