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