testing_data_checker-forestry

testing_data_checker

Check the testing data to do prediction

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

testing_data_checker-forestry function

<dl><dt>object</dt>
<dd>A forestry object.</dd>
<dt>feature.new</dt>
<dd>A data frame of testing predictors.</dd>
<dt>hasNas</dt>
<dd>TRUE if the there were NAs in the training data FALSE otherwise.</dd></dl>

Arguments

Test data check — testing_data_checker-forestry

<dl>

<dt>object</dt>
<dd>A forestry object.</dd>


<dt>feature.new</dt>
<dd>A data frame of testing predictors.</dd>


<dt>hasNas</dt>
<dd>TRUE if the there were NAs in the training data FALSE otherwise.</dd>

</dl>

testing_data_checker-forestry: Test data check

Description

Usage

Arguments