importance_plot_rf

graphs the importance of variables for the random forest model according to the percentage increase in mean square error.

Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.

Oldemar Rodriguez

regressoR

Regression Data Analysis System

Diego Jimenez A.

importance_plot_rf function

<dl><dt>model.rf</dt>
<dd>a random forest model.</dd>
<dt>titles</dt>
<dd>labels on the chart</dd></dl>

Arguments

Ariel Arroyo &lt;luis.ariel.arroyo@promidat.com&gt;

Author

importance_plot_rf — importance_plot_rf

<dl>

<dt>model.rf</dt>
<dd>a random forest model.</dd>


<dt>titles</dt>
<dd>labels on the chart</dd>

</dl>

importance_plot_rf: importance_plot_rf

Description

Usage

Value

Arguments

Author

See Also