plotBMRSummary

[<code><a rd-options="" href="/link/BenchmarkResult?package=mlr&version=2.9" data-mini-rdoc="mlr::BenchmarkResult">BenchmarkResult</a></code>]
Benchmark result.

[<code><a rd-options="" href="/link/Measure?package=mlr&version=2.9" data-mini-rdoc="mlr::Measure">Measure</a></code>]
Performance measure.
Default is the first measure used in the benchmark experiment.

measure

[<code>character(1)</code>]
Currently either &#147;none&#148; or &#147;rank&#148;, the latter performing a rank transformation
(with average handling of ties) of the scores per task.
NB: You can add always add <code><a rd-options="ggplot2" href="/link/scale_x_log10?package=mlr&version=2.9&to=ggplot2" data-mini-rdoc="ggplot2::scale_x_log10">scale_x_log10</a></code> to the result to put scores on a log scale.
Default is &#147;none&#148;.

trafo

[<code>character(n.tasks)</code>]  
Character vector with <code>task.ids</code> in new order.

order.tsks

[<code>numeric(1)</code>]
Point size for ggplot2 <code><a rd-options="ggplot2" href="/link/geom_point?package=mlr&version=2.9&to=ggplot2" data-mini-rdoc="ggplot2::geom_point">geom_point</a></code> for data points.
Default is 4.

pointsize

[<code>numeric(1)</code>]
Small vertical jitter to deal with overplotting in case of equal scores.
Default is 0.05.

jitter

[<code>logical{1}</code>]
Whether to use the short name of the learner instead of its ID in labels. Defaults to <code>TRUE</code>.

pretty.names


Creates a scatter plot, where each line refers to a task.
On that line the aggregated scores for all learners are plotted, for that task.
Optionally, you can apply a rank transformation or just use one of ggplot2's transformations
like <code><a rd-options="ggplot2" href="/link/scale_x_log10?package=mlr&version=2.9&to=ggplot2" data-mini-rdoc="ggplot2::scale_x_log10">scale_x_log10</a></code>.


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

plotBMRSummary function

[<code><a rd-options='' href='BenchmarkResult'>BenchmarkResult</a></code>]
Benchmark result.

[<code><a rd-options='' href='Measure'>Measure</a></code>]
Performance measure.
Default is the first measure used in the benchmark experiment.

[<code>character(1)</code>]
Currently either &#147;none&#148; or &#147;rank&#148;, the latter performing a rank transformation
(with average handling of ties) of the scores per task.
NB: You can add always add <code><a rd-options='ggplot2' href='scale_x_log10'>scale_x_log10</a></code> to the result to put scores on a log scale.
Default is &#147;none&#148;.

[<code>numeric(1)</code>]
Point size for ggplot2 <code><a rd-options='ggplot2' href='geom_point'>geom_point</a></code> for data points.
Default is 4.


Creates a scatter plot, where each line refers to a task.
On that line the aggregated scores for all learners are plotted, for that task.
Optionally, you can apply a rank transformation or just use one of ggplot2's transformations
like <code><a rd-options='ggplot2' href='scale_x_log10'>scale_x_log10</a></code>.


plotBMRSummary: Plot a benchmark summary.

Description

Usage

Arguments

Value

See Also

Examples