getFeatureImportanceLearner.regr.randomForestSRC

getFeatureImportanceLearner

(<a rd-options="" href="/link/Learner?package=mlr&version=2.13" data-mini-rdoc="mlr::Learner">Learner</a> | <code>character(1)</code>)
The learner.

.learner

(<a rd-options="" href="/link/WrappedModel?package=mlr&version=2.13" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a>)
The model.

.model

(any)
Additional parameters, which are passed to the underlying importance value
generating function.

This function is mostly for internal usage. To calculate feature importance use <a rd-options="" href="/link/getFeatureImportance?package=mlr&version=2.13" data-mini-rdoc="mlr::getFeatureImportance">getFeatureImportance</a>.
The return value is a named numeric vector. There does not need to be one value for each feature in the dataset.
In <a rd-options="" href="/link/getFeatureImportance?package=mlr&version=2.13" data-mini-rdoc="mlr::getFeatureImportance">getFeatureImportance</a> missing features will get an importance of zero and if the vector contains <code>NA</code>
they will also be replaced with zero.

internal

Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.

Bernd Bischl

Machine Learning in R

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Patrick Schratz

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Steve Bronder

Alexander Engelhardt

Christoph Molnar

getFeatureImportanceLearner.regr.randomForestSRC function

(<a rd-options='' href='Learner'>Learner</a> | <code>character(1)</code>)
The learner.

(<a rd-options='' href='WrappedModel'>WrappedModel</a>)
The model.

This function is mostly for internal usage. To calculate feature importance use <a rd-options='' href='getFeatureImportance'>getFeatureImportance</a>.
The return value is a named numeric vector. There does not need to be one value for each feature in the dataset.
In <a rd-options='' href='getFeatureImportance'>getFeatureImportance</a> missing features will get an importance of zero and if the vector contains <code>NA</code>
they will also be replaced with zero.

Calculates feature importance values for a given learner. — getFeatureImportanceLearner.regr.randomForestSRC

Calculates feature importance values for a given learner.

getFeatureImportanceLearner.regr.randomForestSRC: Calculates feature importance values for a given learner.

Description

Usage

Arguments

Value