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#
# | 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 | minPurityIncrease | 1e-7 | float | The minimum increase in purity necessary to continue splitting a subtree. |
4 | maxFeatures | Auto | int | The max number of feature columns to consider when determining a best split. |
Example#
use Rubix\ML\Classifiers\ExtraTreeClassifier;
$estimator = new ExtraTreeClassifier(50, 3, 1e-7, 10);
Additional Methods#
Return the number of levels in the tree.
public height() : ?int
Return a factor that quantifies the skewness of the distribution of nodes in the tree.
public balance() : ?int
References#
-
P. Geurts et al. (2005). Extremely Randomized Trees. ↩
Last update:
2021-04-25