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Classification Tree#

A binary tree-based learner that greedily constructs a decision map for classification that minimizes the Gini impurity among the training labels within the leaf nodes. Classification Trees also serve as the base learner of ensemble methods such as Random Forest and AdaBoost.

Interfaces: Estimator, Learner, Probabilistic, Persistable

Data Type Compatibility: Categorical, Continuous

Parameters#

# Param Default Type Description
1 max depth PHP_INT_MAX int The maximum depth of a branch in the tree.
2 max leaf size 3 int The max number of samples that a leaf node can contain.
3 max features Auto int The max number of feature columns to consider when determining a best split.
4 min purity increase 1e-7 float The minimum increase in purity necessary for a node not to be post pruned.

Additional Methods#

Return the normalized feature importances i.e. the proportion that each feature contributes to the overall model, indexed by feature column:

public featureImportances() : array

Return a human readable text representation of the decision tree rules:

public rules() : string

Return the height of the tree:

public height() : int

Return the balance factor of the tree:

public balance() : int

Example#

use Rubix\ML\Classifiers\ClassificationTree;

$estimator = new ClassificationTree(10, 7, 4, 0.01);

References:#

  • W. Y. Loh. (2011). Classification and Regression Trees.
  • K. Alsabti. et al. (1998). CLOUDS: A Decision Tree Classifier for Large Datasets.