best,tuningresult-method

best

An object of class <code><a rd-options="" href="/link/tuningresult?package=CMA&version=1.30.0" data-mini-rdoc="CMA::tuningresult">tuningresult</a></code>.

object


  In this package hyperparameter tuning is performed by
  an inner cross-validation step for each <code>learningset</code>.
  A grid of values is tried and evaluated in terms of the
  misclassification rate, the results are saved in an object
  of class <code><a rd-options="" href="/link/tuningresult?package=CMA&version=1.30.0" data-mini-rdoc="CMA::tuningresult">tuningresult</a></code>. This method displays
  (separately for each <code>learningset</code>) the hyperparameter/
  hyperparameter combination that showed the best results. Note
  that this must not be unique; in this case, only one combination
  is displayed.


multivariate

This package provides a comprehensive collection of various microarray-based classification algorithms
both from Machine Learning and Statistics.
Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed
combined or stepwise in a user-friendly environment.

Christoph Bernau

Synthesis of microarray-based classification

best function

An object of class <code><a href="tuningresult" rd-options="">tuningresult</a></code>.

In this package hyperparameter tuning is performed by
  an inner cross-validation step for each <code>learningset</code>.
  A grid of values is tried and evaluated in terms of the
  misclassification rate, the results are saved in an object
  of class <code><a href="tuningresult" rd-options="">tuningresult</a></code>. This method displays
  (separately for each <code>learningset</code>) the hyperparameter/
  hyperparameter combination that showed the best results. Note
  that this must not be unique; in this case, only one combination
  is displayed.

best: Show best hyperparameter settings

Description

Usage

Arguments

Value

See Also