doMulti: Perform Multiple "1 vs. all" Tasks

In exprso: Rapid Deployment of Machine Learning Algorithms

Description

A function to execute multiple "1 vs. all" binary tasks.

Usage

doMulti(object, top = 0, method, ...)

Arguments

object	An ExprsArray object. The training set.
top	A numeric scalar or character vector. A numeric scalar indicates the number of top features that should undergo feature selection. A character vector indicates specifically which features by name should undergo feature selection. Set top = 0 to include all features. A numeric vector can also be used to indicate specific features by location, similar to a character vector.
method	A character string. The method to apply.
...	Arguments passed to the detailed function.

Details

doMulti runs once for each factor level in the "defineCase" column. If a training set is missing any one of the factor levels (e.g., owing to random cuts during cross-validation), the ExprsModule component that would refer to that class label gets replaced with an NA placeholder. Note that this NA placeholder will prevent a classifier from possibly predicting the NA class (i.e., a classifier can only make predictions about class labels that it "knows"). However, these "unknown" classes still impact metrics of classifier performance. Otherwise, see exprso-predict.

Value

A list of the results from method.

exprso documentation built on May 1, 2019, 7:11 p.m.