Trains the model for 1 or 2 selected features, then displays it via <code><a href="/link/ggplot?package=mlr&version=2.3&to=ggplot2" rd-options="ggplot2" data-mini-rdoc="ggplot2::ggplot">ggplot</a></code>.
Good for teaching or exploring models.For classification and clustering, only 2D plots are supported. The data points, the classification and
potentially through color alpha blending the posterior probabilities are shown.For regression, 1D and 2D plots are supported. 1D shows the data, the estimated mean and potentially
the estimated standard error. 2D does not show estimated standard error,
but only the estimated mean via background color.The plot title displays the model id, its parameters, the training performance
and the cross-validation performance.

plotLearnerPrediction

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.

plotLearnerPrediction: Visualizes a learning algorithm on a 1D or 2D data set.

Description

Usage

Arguments

Value