list_metrics

<p>List all available performance metrics.</p>

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 an efficient permutation-based variable importance measure as
well as novel approaches based on partial dependence plots (PDPs) and
individual conditional expectation (ICE) curves which are described in
Greenwell et al. (2018) <arXiv:1805.04755>. An experimental method for
quantifying the relative strength of interaction effects is also included (see
the previous reference for details).

list_metrics: List metrics

Description

Usage

Arguments

Examples