GenAlgLMEvaluator-class

GenAlgLMEvaluator

Provides a genetic algorithm for finding variable
subsets in high dimensional data with high prediction performance. The
genetic algorithm can use ordinary least squares (OLS) regression models or
partial least squares (PLS) regression models to evaluate the prediction
power of variable subsets. By supporting different cross-validation
schemes, the user can fine-tune the tradeoff between speed and quality of
the solution.

David Kepplinger

gaselect

Genetic Algorithm (GA) for Variable Selection from
High-Dimensional Data

GenAlgLMEvaluator-class function

<dl>
<dt><code>statistic</code></dt>
<dd>The statistic used to evaluate the fitness.</dd></dl>
<dt><code>statisticId</code></dt>
<dd>The (internal) numeric ID of the statistic.</dd>
<dt><code>numThreads</code></dt>
<dd>The maximum number of threads the algorithm is allowed to spawn (a value less than 1 or NULL means no threads).</dd>

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LM Evaluator — GenAlgLMEvaluator-class

<dl>
<dt><code>statistic</code></dt>
<dd>The statistic used to evaluate the fitness.</dd>


<dt><code>statisticId</code></dt>
<dd>The (internal) numeric ID of the statistic.</dd>


<dt><code>numThreads</code></dt>
<dd>The maximum number of threads the algorithm is allowed to spawn (a value less than 1 or NULL means no threads).</dd>


</dl>

GenAlgLMEvaluator-class: LM Evaluator

Description

Arguments

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