class turicreate.decision_tree_regression.DecisionTreeRegression(proxy)

The prediction is based on a collection of base learners, regression trees. This algorithm is a special case for boosted trees regression with number of trees set to 1.

Different from linear models, e.g. linear regression, the gradient boost trees model is able to model non-linear interactions between the features and the target using decision trees as the subroutine. It is good for handling numerical features and categorical features with tens of categories but is less suitable for highly sparse features such as text data.

This model cannot be constructed directly. Instead, use turicreate.decision_tree_regression.create() to create an instance of this model. A detailed list of parameter options and code samples are available in the documentation for the create function.

See also



DecisionTreeRegression.evaluate(dataset[, …]) Evaluate the model on the given dataset.
DecisionTreeRegression.export_coreml(filename) Export the model in Core ML format.
DecisionTreeRegression.extract_features(dataset) For each example in the dataset, extract the leaf indices of each tree as features.
DecisionTreeRegression.get_feature_importance() Get the importance of features used by the model.
DecisionTreeRegression.predict(dataset[, …]) Predict the target column of the given dataset.
DecisionTreeRegression.save(location) Save the model.
DecisionTreeRegression.summary([output]) Print a summary of the model.