plotResiduals

(<a rd-options="" href="/link/Prediction?package=mlr&version=2.15.0" data-mini-rdoc="mlr::Prediction">Prediction</a> | <a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.15.0" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a>)
Input data.

Type of plot. Can be &#8220;scatterplot&#8221;, the default. Or
&#8220;hist&#8221;, for a histogram, or in case of classification problems
a barplot, displaying the residuals.

type

(<code>logical(1)</code>)
Should a loess smoother be added to the plot? Defaults to <code>TRUE</code>.
Only applicable for regression tasks and if <code>type</code> is set to <code>scatterplot</code>.

loess.smooth

(<code>logical(1)</code>)
Should marginal distributions be added to the plot? Defaults to <code>TRUE</code>.
Only applicable for regression tasks and if <code>type</code> is set to <code>scatterplot</code>.

(<code>logical(1)</code>)
Whether to use the short name of the learner instead of its ID in labels.
Defaults to <code>TRUE</code>. 
Only applicable if a <a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.15.0" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a>
is passed to <code>obj</code> in the function call, ignored otherwise.

pretty.names

Plots for model diagnostics. Provides scatterplots of true vs. predicted values
and histograms of the model's residuals.

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.

Patrick Schratz

Machine Learning in R

Bernd Bischl

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

Steve Bronder

Alexander Engelhardt

Christoph Molnar

plotResiduals function

(<a rd-options='' href='Prediction'>Prediction</a> | <a rd-options='' href='BenchmarkResult'>BenchmarkResult</a>)
Input data.

(<code>logical(1)</code>)
Whether to use the short name of the learner instead of its ID in labels.
Defaults to <code>TRUE</code>. 
Only applicable if a <a rd-options='' href='BenchmarkResult'>BenchmarkResult</a>
is passed to <code>obj</code> in the function call, ignored otherwise.

plotResiduals: Create residual plots for prediction objects or benchmark results.

Description

Usage

Arguments

Value

See Also