setMeasurePars

(<a rd-options="" href="/link/Measure?package=mlr&version=2.12.1" data-mini-rdoc="mlr::Measure">Measure</a>)
Performance measure.

measure

(any)
Named (hyper)parameters with new settings. Alternatively these can be passed
using the <code>par.vals</code> argument.

(<a rd-options="" href="/link/list?package=mlr&version=2.12.1" data-mini-rdoc="mlr::list">list</a>)
Optional list of named (hyper)parameter settings. The arguments in
<code>...</code> take precedence over values in this list.

par.vals

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

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

Patrick Schratz

Steve Bronder

setMeasurePars function

(<a rd-options='' href='Measure'>Measure</a>)
Performance measure.

(<a rd-options='' href='list'>list</a>)
Optional list of named (hyper)parameter settings. The arguments in
<code>...</code> take precedence over values in this list.

Set parameters of performance measures — setMeasurePars

Set parameters of performance measures

setMeasurePars: Set parameters of performance measures

Description

Usage

Arguments

Value

See Also