[source] # 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](random-forest.md) and [AdaBoost](adaboost.md). **Interfaces:** [Estimator](../estimator.md), [Learner](../learner.md), [Probabilistic](../probabilistic.md), [Ranks Features](../ranks-features.md), [Persistable](../persistable.md) **Data Type Compatibility:** Categorical, Continuous ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | maxHeight | PHP_INT_MAX | int | The maximum height of the tree. | | 2 | maxLeafSize | 3 | int | The max number of samples that a leaf node can contain. | | 3 | maxFeatures | Auto | int | The max number of feature columns to consider when determining a best split. | | 4 | minPurityIncrease | 1e-7 | float | The minimum increase in purity necessary for a node *not* to be post pruned during tree growth. | ## Example ```php 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: ```php public rules(?array $header = null) : string ``` ```php echo $estimator->rules(['age', 'height', 'income']); ``` ```sh ├─── 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: ```php public height() : int ``` Return the balance factor of the tree: ```php public balance() : int ``` ## References: [^1]: W. Y. Loh. (2011). Classification and Regression Trees. [^2]: K. Alsabti. et al. (1998). CLOUDS: A Decision Tree Classifier for Large Datasets.