plotTuneMultiCritResultGGVIS

[<code><a rd-options="" href="/link/TuneMultiCritResult?package=mlr&version=2.10" data-mini-rdoc="mlr::TuneMultiCritResult">TuneMultiCritResult</a></code>]
Result of <code><a rd-options="" href="/link/tuneParamsMultiCrit?package=mlr&version=2.10" data-mini-rdoc="mlr::tuneParamsMultiCrit">tuneParamsMultiCrit</a></code>.

[<code>logical(1)</code>]
Visualize all evaluated points (or only the non-dominated pareto front)?
Points are colored according to their location.
Default is <code>TRUE</code>.

path

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.

Bernd Bischl

Machine Learning in R

plotTuneMultiCritResultGGVIS function

[<code><a rd-options='' href='TuneMultiCritResult'>TuneMultiCritResult</a></code>]
Result of <code><a rd-options='' href='tuneParamsMultiCrit'>tuneParamsMultiCrit</a></code>.

plotTuneMultiCritResultGGVIS: Plots multi-criteria results after tuning using ggvis.

Description

Usage

Arguments

Value

See Also

Examples