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library(caret)
library(tidyverse)
library(MLeval)
source("./data_prep.R")
data_list <- sets_partitions
results <- list()
models <- c("rrlda", "naive_bayes", "rf", "regLogistic")
fitControl <- trainControl( ## 10-fold CV
method = "repeatedcv",
number = 10,
classProbs = TRUE,
savePredictions = TRUE,
repeats = 2
)
for (model in models) {
dataset = 1
# for (data in data_list) {
for (data in data_list[c(14, 16, 19)]) {
print(paste("Training", model, "on dataset", dataset))
train <- data[["train"]]
X_train <- as.data.frame(train[-c(1, 2)])
Y_train <- train[c(2)][[1]]
levels(Y_train) <- c("Low", "High")
set.seed(13121994)
model_trained <- train(
X_train,
y = Y_train,
method = model,
trControl = fitControl
)
results[[model]][[dataset]] <- model_trained
dataset = dataset + 1
}
}
save(results, file="./modelling_results_withrrlda.RData")
# save(results, file="./modelling_results.RData")
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