plotImportance

A vertical bar chart of features vs. feature importance. 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

plotImportance function

<dl><dt>x</dt>
<dd>a <code>spFSR</code> object</dd>
<dt>low</dt>
<dd>Color for the lowest importance. The default is darkblue.</dd>
<dt>high</dt>
<dd>Color for the highest importance. The default is black.</dd></dl>

Arguments

Ploting importance ranks of best performing features from a spFSR object — plotImportance

<dl>

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


<dt>low</dt>
<dd>Color for the lowest importance. The default is darkblue.</dd>


<dt>high</dt>
<dd>Color for the highest importance. The default is black.</dd>

</dl>

plotImportance: Ploting importance ranks of best performing features from a spFSR object

Description

Usage

Value

Arguments

See Also