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Regression Tree

A decision tree based on the CART (Classification and Regression Tree) learning algorithm that performs greedy splitting by minimizing the variance of the labels at each node split. Regression Trees can be used on their own or as the booster in algorithms such as Gradient Boost.

Interfaces: Estimator, Learner, Ranks Features, Persistable

Data Type Compatibility: Categorical, Continuous

Parameters

#	Name	Default	Type	Description
1	maxHeight	PHP_INT_MAX	int	The maximum height of the tree.
2	maxLeafSize	3	int	The max number of samples that a leaf node can contain.
3	maxFeatures	Auto	int	The max number of feature columns to consider when determining a best split.
4	minPurityIncrease	1e-7	float	The minimum increase in purity necessary for a node not to be post pruned during tree growth.

Example

use Rubix\ML\Regressors\RegressionTree;

$estimator = new RegressionTree(20, 2, null, 1e-3);

Additional Methods

Return a human-readable text representation of the decision tree ruleset:

public rules(?array $header = null) : string

Return the height of the tree i.e. the number of layers:

public height() : int

Return the balance factor of the tree:

public balance() : int

References:

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1. W. Y. Loh. (2011). Classification and Regression Trees. ↩

2. K. Alsabti. et al. (1998). CLOUDS: A Decision Tree Classifier for Large Datasets. ↩

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Last update: 2021-03-03