Phpml\Classification\Ensemble\Bagging Class Reference

Inheritance diagram for Phpml\Classification\Ensemble\Bagging:

Public Member Functions
	__construct (int $numClassifier=50)
	Creates an ensemble classifier with given number of base classifiers Default number of base classifiers is 50.
	predict (array $samples)
	setClassifer (string $classifier, array $classifierOptions=[])
	This method is used to set the base classifier.
	setSubsetRatio (float $ratio)
	This method determines the ratio of samples used to create the 'bootstrap' subset, e.g., random samples drawn from the original dataset with replacement (allow repeats), to train each base classifier.
	train (array $samples, array $targets)

Protected Member Functions
	getRandomSubset (int $index)
	initClassifiers ()
	initSingleClassifier (Classifier $classifier)
	predictSample (array $sample)

Protected Attributes
string	$classifier = DecisionTree::class
array	$classifierOptions = ['depth' => 20]
array	$classifiers = []
int	$featureCount = 0
int	$numClassifier
int	$numSamples
float	$subsetRatio = 0.7

Constructor & Destructor Documentation

__construct()

Phpml\Classification\Ensemble\Bagging::__construct

(

int

$numClassifier = 50

)

Creates an ensemble classifier with given number of base classifiers Default number of base classifiers is 50.

The more number of base classifiers, the better performance but at the cost of procesing time

Reimplemented in Phpml\Classification\Ensemble\RandomForest.

Member Function Documentation

initSingleClassifier()

Phpml\Classification\Ensemble\Bagging::initSingleClassifier ( Classifier $classifier )

protected

Reimplemented in Phpml\Classification\Ensemble\RandomForest.

predict()

Phpml\Estimator::predict ( array $samples )

inherited

Implemented in Phpml\Pipeline.

predictSample()

Phpml\Classification\Ensemble\Bagging::predictSample ( array $sample )

protected

Return values

mixed

setClassifer()

Phpml\Classification\Ensemble\Bagging::setClassifer	(	string	$classifier,
		array	$classifierOptions = [] )

This method is used to set the base classifier.

Default value is DecisionTree\class, but any class that implements the Classifier can be used.
While giving the parameters of the classifier, the values should be given in the order they are in the constructor of the classifier and parameter names are neglected.

Return values

$this

Reimplemented in Phpml\Classification\Ensemble\RandomForest.

setSubsetRatio()

Phpml\Classification\Ensemble\Bagging::setSubsetRatio

(

float

$ratio

)

This method determines the ratio of samples used to create the 'bootstrap' subset, e.g., random samples drawn from the original dataset with replacement (allow repeats), to train each base classifier.

Return values

$this

Exceptions

InvalidArgumentException

train()

Phpml\Classification\Ensemble\Bagging::train	(	array	$samples,
		array	$targets )

Implements Phpml\Estimator.

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The documentation for this class was generated from the following file:

• lib/mlbackend/php/phpml/src/Phpml/Classification/Ensemble/Bagging.php