getImportance

This returns importance ranks of best performing features. See spFeatureSelection for example.

An implementation of feature selection, weighting and ranking via simultaneous perturbation
stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of
features that yield the best predictive performance using some error measures such as mean
squared error (for regression problems) and accuracy rate (for classification problems).

David Akman

spFSR

Feature Selection and Ranking via Simultaneous Perturbation
Stochastic Approximation

Babak Abbasi

Yong Kai Wong

Guo Feng Anders Yeo

Zeren D. Yenice

getImportance function

<dl><dt>x</dt>
<dd>a <code>spFSR</code> object</dd></dl>

Arguments

Extracting feature importance data from a spFSR object — getImportance

<dl>

<dt>x</dt>
<dd>a <code>spFSR</code> object</dd>

</dl>

getImportance: Extracting feature importance data from a spFSR object

Description

Usage

Value

Arguments

See Also