Function call (replace with your function call):

library(ClassificationEnsembles)

Classification(data = ISLR::Carseats,
               colnum = 7,
               numresamples = 25,
               predict_on_new_data = "N",
               save_all_plots = "N",
               set_seed = "N",
               how_to_handle_strings = 1,
               remove_VIF_above <- 5.00,
               save_all_trained_models = "N",
               scale_all_numeric_predictors_in_data = "N",
               use_parallel = "N",
               train_amount = 0.50,
               test_amount = 0.25,
               validation_amount = 0.25)

Discussion of function call here. (For example, the code above randomly resamples the data 25 times, and sets train = 0.50, test = 0.25, validation = 0.25, you might want to discuss other aspects of the function call. For example, the function call does not set a seed, so the results are random.)

List of models (individual models first):

C50:

C50_train_fit <- C50::C5.0(as.factor(y_train) ~ ., data = train)

Linear:

linear_train_fit <- MachineShop::fit(y ~ ., data = train01, model = "LMModel")

Partial Least Squares:

pls_train_fit <- MachineShop::fit(y ~ ., data = train01, model = "PLSModel")

Penalized Discriminant Analysis

pda_train_fit <- MachineShop::fit(y ~ ., data = train01, model = "PDAModel")

RPart:

rpart_train_fit <- MachineShop::fit(y ~ ., data = train01, model = "RPartModel")

Trees:

tree_train_fit <- tree::tree(y_train ~ ., data = train)

How the ensemble is made:

ensemble1 <- data.frame(
    "C50" = c(C50_test_pred, C50_validation_pred),
    "Linear" = c(linear_test_pred, linear_validation_pred),
    "Partial_Least_Squares" = c(pls_test_pred, pls_validation_pred),
    "Penalized_Discriminant_Analysis" = c(pda_test_pred, pda_validation_pred),
    "RPart" = c(rpart_test_pred, rpart_validation_pred),
    "Trees" = c(tree_test_pred, tree_validation_pred)
  )

ensemble_row_numbers <- as.numeric(row.names(ensemble1))
ensemble1$y <- df[ensemble_row_numbers, "y"]

ensemble1 <- ensemble1[complete.cases(ensemble1), ]

Ensemble Bagged Cart:

ensemble_bag_cart_train_fit <- ipred::bagging(y ~ ., data = ensemble_train)

Ensemble Bagged Random Forest:

ensemble_bag_train_rf <- randomForest::randomForest(ensemble_y_train ~ ., data = ensemble_train, mtry = ncol(ensemble_train) - 1)

Ensemble C50:

ensemble_C50_train_fit <- C50::C5.0(ensemble_y_train ~ ., data = ensemble_train)

Ensemble Naive Bayes:

ensemble_n_bayes_train_fit <- e1071::naiveBayes(ensemble_y_train ~ ., data = ensemble_train)

Ensemble Support Vector Machines:

ensemble_svm_train_fit <- e1071::svm(ensemble_y_train ~ ., data = ensemble_train, kernel = "radial", gamma = 1, cost = 1)

Ensemble Trees:

ensemble_tree_train_fit <- tree::tree(y ~ ., data = ensemble_train)