preprocess_training

Perform preprocessing for the training data, including
 converting data to dataframe, and encoding categorical data into numerical
 representation.

Provides fast implementations of Random Forests,
Gradient Boosting, and Linear Random Forests, with an emphasis on inference
and interpretability. Additionally contains methods for variable
importance, out-of-bag prediction, regression monotonicity, and
several methods for missing data imputation.

Theo Saarinen

Rforestry

Random Forests, Linear Trees, and Gradient Boosting for
Inference and Interpretability

Sören Künzel

Simon Walter

Sam Antonyan

Edward Liu

Allen Tang

Jasjeet Sekhon

preprocess_training function

<dl><dt>x</dt>
<dd>A data frame of all training predictors.</dd>
<dt>y</dt>
<dd>A vector of all training responses.</dd></dl>

Arguments

preprocess_training — preprocess_training

<dl>

<dt>x</dt>
<dd>A data frame of all training predictors.</dd>


<dt>y</dt>
<dd>A vector of all training responses.</dd>

</dl>

preprocess_training: preprocess_training

Description

Usage

Value

Arguments