importance_randomForest

object

Importance can be assessed in two ways:<dl class="dl-horizontal">
 <dt>1.</dt><dd>Permuted out-of-bag prediction error (default). This can only be
 used if the classifier was fitted with argument
 <code>prediction=TRUE</code> which is default.</dd>
 <dt>2.</dt><dd>Total decrease in node impurity.</dd>
</dl>

type

Feature importance of random forest.

A toolbox for designing and evaluating predictive models with
resampling methods. The aim of this package is to provide a simple and
efficient general framework for working with any type of prediction
problem, be it classification, regression or survival analysis, that is
easy to extend and adapt to your specific setting. Some commonly used
methods for classification, regression and survival analysis are included.

Christofer Backlin

emil

Evaluation of Modeling without Information Leakage

Mats Gustafsson

importance_randomForest function

Importance can be assessed in two ways:<dl class='dl-horizontal'>
 <dt>1.</dt><dd>Permuted out-of-bag prediction error (default). This can only be
 used if the classifier was fitted with argument
 <code>prediction=TRUE</code> which is default.</dd>
 <dt>2.</dt><dd>Total decrease in node impurity.</dd>
</dl>

importance_randomForest: Feature importance of random forest.

Description

Usage

Arguments

Value

See Also