Partial

Deprecated, please use 'FeatureEffect', see ?FeatureEffect

Interpretability methods to analyze the behavior and predictions of
any machine learning model.
Implemented methods are:
Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>,
accumulated local effects plots described by Apley (2018) <arXiv:1612.08468>,
partial dependence plots described by Friedman (2001) <http://www.jstor.org/stable/2699986>,
individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>,
local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>,
the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>,
feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and
tree surrogate models.

Christoph Molnar

Interpretable Machine Learning

Partial function

<code><a rd-options="" href="/link/R6Class?package=iml&version=0.9.0" data-mini-rdoc="iml::R6Class">R6Class</a></code> object.

Format

Effect of one or two feature(s) on the model predictions (deprecated) — Partial

<code><a rd-options='' href='R6Class'>R6Class</a></code> object.

Partial: Effect of one or two feature(s) on the model predictions (deprecated)

Description

Arguments

Format

See Also