Classification {ClassificationEnsembles}R Documentation

classification—function to perform classification analysis and return results to the user.

Description

classification—function to perform classification analysis and return results to the user.

Usage

Classification(
  data,
  colnum,
  numresamples,
  predict_on_new_data = c("Y", "N"),
  remove_VIF_above,
  scale_all_numeric_predictors_in_data,
  how_to_handle_strings = c(0("No strings"), 1("Strings as factors")),
  save_all_trained_models = c("Y", "N"),
  save_all_plots,
  use_parallel = c("Y", "N"),
  train_amount,
  test_amount,
  validation_amount
)

Arguments

data

a data set that includes classification data. For example, the Carseats data in the ISLR package

colnum

the number of the column. For example, in the Carseats data this is column 7, ShelveLoc with three values, Good, Medium and Bad

numresamples

the number of times to resample the analysis

predict_on_new_data

Gives the user the opportunity to use the trained models to predict on new and untrained data

remove_VIF_above

Removes columns with Variance Inflaction Factors above the level chosen by the user

scale_all_numeric_predictors_in_data

Scales all numeric predictors in the original data

how_to_handle_strings

Converts strings to factor levels

save_all_trained_models

Gives the user the option to save all trained models in the Environment

save_all_plots

Saves all plots in the user's chosen format

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 full analysis, including data visualizations, statistical summaries, and a full report on the results of 35 models on the data


[Package ClassificationEnsembles version 0.5.0 Index]