R code for boosting tune

hyper_grid <- expand.grid(
  shrinkage = c(.01, .1, .3),
  interaction.depth = c(1, 3, 5),
  optimal_trees = 0,               
  min_RMSE = 0               
)
nrow(hyper_grid)
# We loop through each hyperparameter combination and apply 8000 trees. 
# randomize data
random_index <- sample(1:nrow(train_CO2), nrow(train_CO2))
random_CO2_train <- train_CO2[random_index, ]

for(i in 1:nrow(hyper_grid)) {
  # train model
  gbm.tune <- gbm(
    formula=CO2~MemberState+MfrHarmonised+ApprovalYr+Year+FuelType+Mass
    +EngineSize+Power,
    distribution = "gaussian",
    data = random_CO2_train,
    n.trees = 8000,
    interaction.depth = hyper_grid$interaction.depth[i],
    shrinkage = hyper_grid$shrinkage[i],
    train.fraction = .75,
    n.cores = NULL, # will use all cores by default
    verbose = FALSE)
  predict(gbm.tune,)
  hyper_grid$optimal_trees[i] <- which.min()
  hyper_grid$min_RMSE[i] <- sqrt(min(gbm.tune$valid.error))
}
hyper_grid %>%dplyr::arrange(min_RMSE) %>%head(10)
# best parameter:shrinkage=0.1,interaction.depth=3,optimal trees=7192