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Extra Tree Classifier#

An Extremely Randomized Classification Tree that recursively chooses node splits with the least entropy among a set of k (given by max features) random split points. Extra Trees are useful in ensembles such as Random Forest or AdaBoost as the weak learner or they can be used on their own. The strength of Extra Trees as compared to standard decision trees are their computational efficiency and lower prediction variance.

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\ExtraTreeClassifier;

$estimator = new ExtraTreeClassifier(50, 3, 4, 1e-7);

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#

  • P. Geurts et al. (2005). Extremely Randomized Trees.