plotPartialPredictionGGVIS

[<code>PartialPredictionData</code>]
Generated by <code><a rd-options="" href="/link/generatePartialPredictionData?package=mlr&version=2.8" data-mini-rdoc="mlr::generatePartialPredictionData">generatePartialPredictionData</a></code>.

[<code>character(1)</code>]
The name of a feature to be mapped to an interactive sidebar using Shiny.
This feature must have been an element of the <code>features</code> argument to
<code><a rd-options="" href="/link/generatePartialPredictionData?package=mlr&version=2.8" data-mini-rdoc="mlr::generatePartialPredictionData">generatePartialPredictionData</a></code> and is only applicable when said argument had length
greater than 1.
If <code><a rd-options="" href="/link/generatePartialPredictionData?package=mlr&version=2.8" data-mini-rdoc="mlr::generatePartialPredictionData">generatePartialPredictionData</a></code> is called with the <code>interaction</code> argument <code>FALSE</code>
(the default) with argument <code>features</code> of length greater than one, then <code>interact</code> is ignored and
the feature displayed is controlled by an interactive side panel.
Default is <code>NULL</code>.

interact

[<code>numeric(1)</code>]
If <code>individual = TRUE</code> then <code>sample</code> allows the user to sample without replacement
from the output to make the display more readable. Each row is sampled with probability <code>p</code>.
Default is <code>1</code>.


Plot a partial prediction from <code><a rd-options="" href="/link/generatePartialPredictionData?package=mlr&version=2.8" data-mini-rdoc="mlr::generatePartialPredictionData">generatePartialPredictionData</a></code> using ggvis.


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.

plotPartialPredictionGGVIS: Plot a partial prediction using ggvis.

Description

Usage

Arguments

Value

See Also