fsBalance

An <code>ExprsArray</code> object to undergo feature selection.

object

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 <code>top = 0</code> to include all features. A numeric vector can also be used
to indicate specific features by location, similar to a character vector.

A numeric scalar. Specifies the number of top features that should get
returned by the feature selection method. Use of <code>keep</code> is generally not
recommended, but can speed up analyses of large data.

keep

A character string. The method used to build
the serial binary partition matrix of balances. Any
<code>balance::sbp.from*</code> function will work.

sbp.how

A boolean. Toggles whether to return balances
representing three components. Argument passed to
<code>balance::sbp.subset</code>. Set <code>ternary = FALSE</code> and
<code>ratios = FALSE</code> to skip subset.

ternary

A boolean. Toggles whether to return balances
representing two components. Argument passed to
<code>balance::sbp.subset</code>. Set <code>ternary = FALSE</code> and
<code>ratios = FALSE</code> to skip subset.

ratios

Arguments passed to the detailed function.

<code>fsBalance</code> converts features into balances.

Supervised machine learning has an increasingly important role in data
analysis. This package introduces a framework for rapidly building and deploying
supervised machine learning in a high-throughput manner. This package provides a
user-friendly interface that empowers investigators to execute state-of-the-art
binary and multi-class classification, as well as regression, with minimal
programming experience necessary.

fsBalance: Convert Features into Balances

Description

Usage

Arguments

Value