A binary tree-based learner that greedily constructs a decision map for classification that minimizes the Gini impurity among the training labels during splitting and within leaf nodes. 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
Display a human readable text representation of the decision tree:
public printrules() : void
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.