Classification: 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"),
  set_seed = 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
)

Value

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

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: asks if the user has new data to be analyzed using the trained models that were just developed
set_seed: Gives the user the option to set a seed
remove_VIF_above: Removes columns with Variance Inflation 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