To build a Gradient Boosted Trees Regression model using methods of the Model Builder class of Gradient Boosted Tree Regression, complete the following steps:
- Create a Gradient Boosted Tree Regression model builder using a constructor with the required number of classes and trees.
- Create a decision tree and add nodes to it:
- Use the createTree method with the required number of nodes in a tree and a label of the class for which the tree is created.
- Use the addSplitNode and addLeafNode methods to add split and leaf nodes to the created tree. See the note below describing the decision tree structure.
- After you add all nodes to the current tree, proceed to creating the next one in the same way.
- Use the getModel method to get the trained Gradient Boosted Trees Regression model after all trees have been created.
Each tree consists of internal nodes (called non-leaf or split nodes) and external nodes (leaf nodes). Each split node denotes a feature test that is a Boolean expression, for example, f < featureValue or f = featureValue, where f is a feature and featureValue is a constant. The test type depends on the feature type: continuous, categorical, or ordinal. For more information on the test types, see Algorithms > Training and Prediction > Classification and Regression > Decision Tree > Details.
The inducted decision tree is a binary tree, meaning that each non-leaf node has exactly two branches: true and false. Each split node contains featureIndex, the index of the feature used for the feature test in this node, and featureValue, the constant for the Boolean expression in the test. Each leaf node contains a classLabel, the predicted class for this leaf. For more information on decision trees, see Algorithms > Training and Prediction > Classification and Regression > Decision Tree.
Add nodes to the created tree in accordance with the pre-calculated structure of the tree. Check that the leaf nodes do not have children nodes and that the splits have exactly two children.