utils/examples/random_forest.R

In ArjunChattoraj10/imbDis: Imbalanced Discrimination

# Random Forest Classification

# Load caret and imbDis
library(caret)
library(imbDis)

# Ensure presence of file dependencies - train/test split data
source("utils/examples/train_test.R")

set.seed(225)

# random forest with 7 predictors
tc = trainControl(method = "cv", number = 10)
RF_7 = train(factor(y) ~ x1+x2+x3+x4+x5+x6+x7, data = train_dat, method = "rf", trControl = tc)

# random forest with all predictors
RF_all = train(factor(y) ~ ., data = train_dat, method = "rf", trControl = tc)

# obtain predicted probabilities - 7 preds
preds_RF_7 = predict(RF_7$finalModel, test_dat, type = "prob")[,2]

# obtain predicted probabilities - all preds
preds_RF_all = predict(RF_all$finalModel, test_dat, type = "prob")[,2]

# optional - save the files to csv within data directory
# uncomment code to run

# res_RF = data.frame(test_dat$y, preds_RF_7, preds_RF_all)
# names(res_RF)[1] = "orig"
# write.csv(res_RF, "utils/data/res_RF.csv", row.names = FALSE)

# Define classes
imbD_RF_7 = imbDis(test_dat$y, preds_RF_7, 1)
imbD_RF_all = imbDis(test_dat$y, preds_RF_all, 1)

# Metrics for 7 preds RF
auc(imbD_RF_7)
brier(imbD_RF_7)
logLoss(imbD_RF_7)

# Metrics for all preds RF
auc(imbD_RF_all)
brier(imbD_RF_all)
logLoss(imbD_RF_all)