Numeric: Numeric—function to automatically build 23 individual models...

In NumericEnsembles: Automatically Runs 23 Individual and 17 Ensembles of Models

Numeric—function to automatically build 23 individual models and 17 ensembles then return the results to the user

Description

Numeric—function to automatically build 23 individual models and 17 ensembles then return the results to the user

Usage

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
)

Arguments

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

Value

a real number

NumericEnsembles documentation built on April 12, 2025, 1:22 a.m.