php-ml/docs/machine-learning/feature-selection/selectkbest.md

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2018-02-14 18:51:07 +00:00
# SelectKBest
`SelectKBest` - select features according to the k highest scores.
## Constructor Parameters
* $k (int) - number of top features to select, rest will be removed (default: 10)
* $scoringFunction (ScoringFunction) - function that take samples and targets and return array with scores (default: ANOVAFValue)
```php
use Phpml\FeatureSelection\SelectKBest;
$transformer = new SelectKBest(2);
```
## Example of use
As an example we can perform feature selection on Iris dataset to retrieve only the two best features as follows:
```php
use Phpml\FeatureSelection\SelectKBest;
use Phpml\Dataset\Demo\IrisDataset;
$dataset = new IrisDataset();
$selector = new SelectKBest(2);
$selector->fit($samples = $dataset->getSamples(), $dataset->getTargets());
$selector->transform($samples);
/*
$samples[0] = [1.4, 0.2];
*/
```
## Scores
You can get a array with the calculated score for each feature.
A higher value means that a given feature is better suited for learning.
Of course, the rating depends on the scoring function used.
```
use Phpml\FeatureSelection\SelectKBest;
use Phpml\Dataset\Demo\IrisDataset;
$dataset = new IrisDataset();
$selector = new SelectKBest(2);
$selector->fit($samples = $dataset->getSamples(), $dataset->getTargets());
$selector->scores();
/*
..array(4) {
[0]=>
float(119.26450218451)
[1]=>
float(47.364461402997)
[2]=>
float(1179.0343277002)
[3]=>
float(959.32440572573)
}
*/
```
## Scoring function
Available scoring functions:
For classification:
- **ANOVAFValue**
The one-way ANOVA tests the null hypothesis that 2 or more groups have the same population mean.
The test is applied to samples from two or more groups, possibly with differing sizes.
For regression:
- **UnivariateLinearRegression**
Quick linear model for testing the effect of a single regressor, sequentially for many regressors.
This is done in 2 steps:
- 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)).
- 2. It is converted to an F score
## Pipeline
`SelectKBest` implements `Transformer` interface so it can be used as part of pipeline:
```php
use Phpml\FeatureSelection\SelectKBest;
use Phpml\Classification\SVC;
use Phpml\FeatureExtraction\TfIdfTransformer;
use Phpml\Pipeline;
$transformers = [
new TfIdfTransformer(),
new SelectKBest(3)
];
$estimator = new SVC();
$pipeline = new Pipeline($transformers, $estimator);
```