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, Ranks Features, 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. |
Example#
use Rubix\ML\Regressors\ExtraTreeRegressor;
$estimator = new ExtraTreeRegressor(30, 3, 20, 0.05);
Additional Methods#
Return a human-readable text representation of the decision tree ruleset:
public rules(?array $header = null) : string
Example
echo $estimator->rules(['x', 'y', 'z']);
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.