97 lines
2.5 KiB
Markdown
97 lines
2.5 KiB
Markdown
# SelectKBest
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`SelectKBest` - select features according to the k highest scores.
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## Constructor Parameters
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* $k (int) - number of top features to select, rest will be removed (default: 10)
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* $scoringFunction (ScoringFunction) - function that takes samples and targets and returns an array with scores (default: ANOVAFValue)
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```php
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use Phpml\FeatureSelection\SelectKBest;
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$transformer = new SelectKBest(2);
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```
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## Example of use
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As an example we can perform feature selection on Iris dataset to retrieve only the two best features as follows:
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```php
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use Phpml\FeatureSelection\SelectKBest;
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use Phpml\Dataset\Demo\IrisDataset;
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$dataset = new IrisDataset();
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$selector = new SelectKBest(2);
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$selector->fit($samples = $dataset->getSamples(), $dataset->getTargets());
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$selector->transform($samples);
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/*
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$samples[0] = [1.4, 0.2];
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*/
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```
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## Scores
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You can get an array with the calculated score for each feature.
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A higher value means that a given feature is better suited for learning.
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Of course, the rating depends on the scoring function used.
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```
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use Phpml\FeatureSelection\SelectKBest;
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use Phpml\Dataset\Demo\IrisDataset;
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$dataset = new IrisDataset();
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$selector = new SelectKBest(2);
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$selector->fit($samples = $dataset->getSamples(), $dataset->getTargets());
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$selector->scores();
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/*
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..array(4) {
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[0]=>
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float(119.26450218451)
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[1]=>
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float(47.364461402997)
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[2]=>
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float(1179.0343277002)
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[3]=>
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float(959.32440572573)
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}
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*/
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```
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## Scoring function
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Available scoring functions:
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For classification:
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- **ANOVAFValue**
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The one-way ANOVA tests the null hypothesis that 2 or more groups have the same population mean.
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The test is applied to samples from two or more groups, possibly with differing sizes.
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For regression:
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- **UnivariateLinearRegression**
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Quick linear model for testing the effect of a single regressor, sequentially for many regressors.
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This is done in 2 steps:
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- 1. The cross correlation between each regressor and the target is computed, that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *std(y)).
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- 2. It is converted to an F score
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## Pipeline
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`SelectKBest` implements `Transformer` interface so it can be used as part of pipeline:
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```php
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use Phpml\FeatureSelection\SelectKBest;
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use Phpml\Classification\SVC;
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use Phpml\FeatureExtraction\TfIdfTransformer;
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use Phpml\Pipeline;
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$transformers = [
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new TfIdfTransformer(),
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new SelectKBest(3)
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];
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$estimator = new SVC();
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$pipeline = new Pipeline($transformers, $estimator);
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```
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