metric_mse

metric_rmse

metric_sse

metric_mae

metric_rsquared

metric_accuracy

metric_error

metric_auc

metric_logLoss

metric_mauc

actual

Vector of predicted target values.

predicted

Logical indicating whether or not <code>NA</code> values should be
stripped before the computation proceeds.

na.rm

Common model/evaluation metrics for machine learning.

A general framework for constructing variable importance plots from
various types of machine learning models in R. Aside from some standard model-
specific variable importance measures, this package also provides model-
agnostic approaches that can be applied to any supervised learning algorithm.
These include 1) an efficient permutation-based variable importance measure,
2) variable importance based on Shapley values (Strumbelj and Kononenko,
2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based
approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A
variance-based method for quantifying the relative strength of interaction
effects is also included (see the previous reference for details).

Brandon Greenwell

Variable Importance Plots

Brad Boehmke

Bernie Gray

metric_mse function

<dl><dt>actual</dt>
<dd>Vector of actual target values.</dd>
<dt>predicted</dt>
<dd>Vector of predicted target values.</dd>
<dt>na.rm</dt>
<dd>Logical indicating whether or not <code>NA</code> values should be
stripped before the computation proceeds.</dd></dl>

Arguments

Model metrics — metric_mse

<dl>

<dt>actual</dt>
<dd>Vector of actual target values.</dd>


<dt>predicted</dt>
<dd>Vector of predicted target values.</dd>


<dt>na.rm</dt>
<dd>Logical indicating whether or not <code>NA</code> values should be
stripped before the computation proceeds.</dd>

</dl>

metric_mse: Model metrics

Description

Usage

Arguments

Examples