Extra Tree Regressor#
An Extremely Randomized Regression Tree. These trees differ from standard Regression Trees in that they choose candidate splits at random, rather than searching the entire column for the best split. Extra Trees are faster to build and their predictions have higher variance than a regular decision tree.
Interfaces: Estimator, Learner, 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 number of features 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\Regressors\ExtraTreeRegressor;
$estimator = new ExtraTreeRegressor(30, 3, 20, 0.05);
References#
- P. Geurts et al. (2005). Extremely Randomized Trees.