gbmCMA-methods

gbmCMA,matrix,numeric,missing-method

gbmCMA,matrix,factor,missing-method

gbmCMA,data.frame,missing,formula-method

gbmCMA,ExpressionSet,character,missing-method


 Roughly speaking, Boosting combines 'weak learners'
 in a weighted manner in a stronger ensemble. This
 method calls the function <code>gbm.fit</code> from the
 package <code>gbm</code>. The 'weak learners' are
 simple trees that need only very few splits (default: 1).


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

gbmCMA-methods function

<dl> <dt>X = "matrix", y = "numeric", f = "missing"</dt><dd>signature 1</dd> <dt>X = "matrix", y = "factor", f = "missing"</dt><dd>signature 2</dd> <dt>X = "data.frame", y = "missing", f = "formula"</dt><dd>signature 3</dd> <dt>X = "ExpressionSet", y = "character", f = "missing"</dt><dd>signature 4</dd> </dl> For further argument and output information, consult
<code><a rd-options="" href="/link/gbmCMA?package=CMA&version=1.30.0" data-mini-rdoc="CMA::gbmCMA">gbmCMA</a>.</code>

Methods

Roughly speaking, Boosting combines 'weak learners'
 in a weighted manner in a stronger ensemble. This
 method calls the function <code>gbm.fit</code> from the
 package <code>gbm</code>. The 'weak learners' are
 simple trees that need only very few splits (default: 1).

gbmCMA-methods: Tree-based Gradient Boosting

Description

Arguments

Methods