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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. The height and bushiness of the tree can be determined by the user-defined max depth and max leaf size hyper-parameters. 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.