Numeric—function to automatically build 23 individual models and 17 ensembles then return the results to the user
Numeric(
data,
colnum,
numresamples,
remove_VIF_above = 5,
remove_ensemble_correlations_greater_than = 0.98,
scale_all_predictors_in_data = c("Y", "N"),
data_reduction_method = c(0("none"), 1("BIC exhaustive"), 2("BIC forward"),
3("BIC backward"), 4("BIC seqrep"), 5("Mallows_cp exhaustive"),
6("Mallows_cp forward"), 7("Mallows_cp backward"), 8("Mallows_cp, seqrep")),
ensemble_reduction_method = c(0("none"), 1("BIC exhaustive"), 2("BIC forward"),
3("BIC backward"), 4("BIC seqrep"), 5("Mallows_cp exhaustive"),
6("Mallows_cp forward"), 7("Mallows_cp backward"), 8("Mallows_cp, seqrep")),
how_to_handle_strings = c(0("none"), 1("factor levels"), 2("One-hot encoding"),
3("One-hot encoding with jitter")),
predict_on_new_data = c("Y", "N"),
save_all_trained_models = c("Y", "N"),
save_all_plots = c("Y", "N"),
use_parallel = c("Y", "N"),
train_amount,
test_amount,
validation_amount
)a real number
data can be a CSV file or within an R package, such as MASS::Boston
a column number in your data
the number of resamples
remove columns with Variable Inflation Factor above value chosen by the user
maximum value for correlations of the ensemble
"Y" or "N" to scale numeric data
0(none), BIC (1, 2, 3, 4) or Mallow's_cp (5, 6, 7, 8) for Forward, Backward, Exhaustive and SeqRep
0(none), BIC (1, 2, 3, 4) or Mallow's_cp (5, 6, 7, 8) for Forward, Backward, Exhaustive and SeqRep
0: No strings, 1: Factor values, 2: One-hot encoding, 3: One-hot encoding AND jitter
"Y" or "N". If "Y", then you will be asked for the new data
"Y" or "N". If "Y", then places all the trained models in the Environment
Saves all plots to the working directory
"Y" or "N" for parallel processing
set the amount for the training data
set the amount for the testing data
Set the amount for the validation data