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.
Data Type Compatibility: Categorical, Continuous
|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.|
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
use Rubix\ML\Classifiers\ClassificationTree; $estimator = new ClassificationTree(10, 7, 4, 0.01);
- W. Y. Loh. (2011). Classification and Regression Trees.
- K. Alsabti. et al. (1998). CLOUDS: A Decision Tree Classifier for Large Datasets.