fs: Select Features

In exprso: Rapid Deployment of Machine Learning Algorithms

Description

The exprso package includes these feature selection modules:

- fsSample

- fsNULL

- fsANOVA

- fsInclude

- fsStats

- fsCor

- fsPrcomp

- fsPCA

- fsRDA

- fsEbayes

- fsEdger

- fsMrmre

- fsRankProd

- fsBalance

Details

Considering the high-dimensionality of many datasets, it is prudent and often necessary to prioritize which features to include during model construction. This package provides functions for some of the most frequently used feature selection methods. Each function works as a self-contained wrapper that (1) pre-processes the ExprsArray input, (2) performs the feature selection, and (3) returns an ExprsArray output with an updated feature selection history. These histories get passed along at every step of the way until they eventually get used to pre-process an unlabeled dataset during model deployment (i.e., prediction).

The argument top specifies either the names or the number of features to supply TO the feature selection method, not what the user intends to retrieve FROM the feature selection method. When calling the first feature selection method (or the first build method, if skipping feature selection), a numeric top argument will select a "top ranked" feature set according to their default order in the ExprsArray input.

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