Optimizes the hyperparameters of a learner.
Allows for different optimization methods, such as grid search, evolutionary strategies, etc.
You can select such an algorithm (and its settings)
by passing a corresponding control object. For a complete list of implemented algorithms look at
<code><a href="/link/TuneControl?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::TuneControl">TuneControl</a></code>.<p></p><p>Note that if tranformations are associated with the parameters, the returned result will contain
transformed values in the optimal result and the path.</p>

tuneParams

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also a n experimental extension for surival analysis and cost-sensitive
    learning. Generic resampling, including cross-validation, bootstrapping and
    subsampling. Hyperparameter tuning with modern optimization techniques.
    Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning.

tuneParams: Hyperparameter tuning.

Description

Usage

Arguments

Value

See Also