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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


# 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 minPurityIncrease 1e-7 float The minimum increase in purity necessary to continue splitting a subtree.
4 maxFeatures Auto int The max number of feature columns to consider when determining a best split.
5 maxBins Auto int The maximum number of bins to consider when determining a split with a continuous feature as the split point.


use Rubix\ML\Regressors\RegressionTree;

$estimator = new RegressionTree(20, 2, 1e-3, 10, null);

Additional Methods#

Export a Graphviz "dot" encoding of the decision tree structure.

public exportGraphviz() : Encoding

use Rubix\ML\Helpers\Graphviz;
use Rubix\ML\Persisters\Filesystem;

$dot = $estimator->exportGraphviz();

Graphviz::dotToImage($dot)->saveTo(new Filesystem('tree.png'));

Return the number of levels in the tree.

public height() : ?int

Return a factor that quantifies the skewness of the distribution of nodes in the tree.

public balance() : ?int


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

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