gbmCMA-methods: Tree-based Gradient Boosting

In CMA: Synthesis of microarray-based classification

Description

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

Methods

X = "matrix", y = "numeric", f = "missing"

signature 1

X = "matrix", y = "factor", f = "missing"

signature 2

X = "data.frame", y = "missing", f = "formula"

signature 3

X = "ExpressionSet", y = "character", f = "missing"

signature 4

For further argument and output information, consult gbmCMA.

CMA documentation built on Nov. 8, 2020, 5:02 p.m.