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.