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 height 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, Ranks Features, Persistable
Data Type Compatibility: Categorical, Continuous
Parameters#
| # | Param | Default | Type | Description |
|---|---|---|---|---|
| 1 | max height | PHP_INT_MAX | int | The maximum height of 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 during tree growth. |
Example#
use Rubix\ML\Classifiers\ClassificationTree;
$estimator = new ClassificationTree(10, 7, 4, 0.01);
Additional Methods#
Return a human-readable text representation of the decision tree ruleset:
public rules(?array $header = null) : string
echo $estimator->rules(['age', 'height', 'income']);
├─── age < 70
├───├─── income < 260734.0
├───├───├─── income < 80207.0
├───├───├───├─── height < 182.0
├───├───├───├───├─── Best (outcome=high school impurity=0.19546677755182 n=9)
├───├───├───├─── height >= 182.0
├───├───├───├───├─── Best (outcome=bachelors impurity=-0 n=67)
├───├───├─── income >= 80207.0
├───├───├───├─── Best (outcome=masters impurity=-0 n=77)
├───├─── income >= 260.73460601
├───├───├─── Best (outcome=doctorate impurity=-0 n=49)
├─── age >= 70
├───├─── Best (outcome=high school impurity=-0 n=98)
Return the height of the tree i.e. the number of layers:
public height() : int
Return the balance factor of the tree:
public balance() : int
References:#
- W. Y. Loh. (2011). Classification and Regression Trees.
- K. Alsabti. et al. (1998). CLOUDS: A Decision Tree Classifier for Large Datasets.