Regression Tree#
A decision tree based on the CART (Classification and Regression Tree) learning algorithm that performs greedy splitting by minimizing the variance of the labels at each node split. Regression Trees can be used on their own or as the booster in algorithms such as Gradient Boost.
Interfaces: Estimator, Learner, 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. |
5 | maxBins | Auto | int | The maximum number of bins to consider when determining a split with a continuous feature as the split point. |
Example#
use Rubix\ML\Regressors\RegressionTree;
$estimator = new RegressionTree(20, 2, 1e-3, 10, null);
Additional Methods#
Export a Graphviz "dot" encoding of the decision tree structure.
public exportGraphviz() : Encoding
use Rubix\ML\Helpers\Graphviz;
use Rubix\ML\Persisters\Filesystem;
$dot = $estimator->exportGraphviz();
Graphviz::dotToImage($dot)->saveTo(new Filesystem('tree.png'));
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