plotResiduals

[<code><a rd-options="" href="/link/Prediction?package=mlr&version=2.10" data-mini-rdoc="mlr::Prediction">Prediction</a></code> | <code><a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.10" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a></code>]
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 <code><a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.10" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a></code>
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&#39;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.

Bernd Bischl

Machine Learning in R

plotResiduals function

[<code><a rd-options='' href='Prediction'>Prediction</a></code> | <code><a rd-options='' href='BenchmarkResult'>BenchmarkResult</a></code>]
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 <code><a rd-options='' href='BenchmarkResult'>BenchmarkResult</a></code>
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