plotTuneMultiCritResult

[<code><a rd-options="" href="/link/TuneMultiCritResult?package=mlr&version=2.8" data-mini-rdoc="mlr::TuneMultiCritResult">TuneMultiCritResult</a></code>]
Result of <code><a rd-options="" href="/link/tuneParamsMultiCrit?package=mlr&version=2.8" data-mini-rdoc="mlr::tuneParamsMultiCrit">tuneParamsMultiCrit</a></code>.

[<code>logical(1)</code>]
Visualize all evaluated points (or only the non-dominated pareto front)?
For the full path, the size of the points on the front is slightly increased.
Default is <code>TRUE</code>.

path

[<code>character(1)</code>]
Which column of <code>res$opt.path</code> should be mapped to ggplot2 color?
Default is <code>NULL</code>, which means none.

[<code>character(1)</code>]
Which column of <code>res$opt.path</code> should be mapped to ggplot2 shape?
Default is <code>NULL</code>, which means none.

shape

[<code>numeric(1)</code>]
Point size for ggplot2 <code><a rd-options="ggplot2" href="/link/geom_point?package=mlr&version=2.8&to=ggplot2" data-mini-rdoc="ggplot2::geom_point">geom_point</a></code> for data points.
Default is 2.

pointsize


Visualizes the pareto front and possibly the dominated points.


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.

plotTuneMultiCritResult: Plots multi-criteria results after tuning using ggplot2.

Description

Usage

Arguments

Value

See Also

Examples