API Reference

Abstract type encompassing all optimal tree learners.

Learner for training Optimal Classification Trees.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• shared OptimalTreeLearner parameters

Learner for training Optimal Regression Trees.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• regression Learner parameters
• shared OptimalTreeLearner parameters
• regression OptimalTreeLearner parameters

Learner for training Optimal Survival Trees.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• survival Learner parameters
• shared OptimalTreeLearner parameters
• survival OptimalTreeLearner parameters

Learner for training Optimal Prescriptive Trees where the prescriptions should aim to minimize outcomes.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• regression Learner parameters
• prescription Learner parameters
• shared OptimalTreeLearner parameters
• regression OptimalTreeLearner parameters
• prescription OptimalTreeLearner parameters

Learner for training Optimal Prescriptive Trees where the prescriptions should aim to maximize outcomes.

Supports the same parameters as OptimalTreePrescriptionMinimizer.

Learner for training Optimal Policy Trees where the prescriptions should aim to minimize outcomes.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• policy Learner parameters
• shared OptimalTreeLearner parameters

Learner for training Optimal Policy Trees where the prescriptions should aim to maximize outcomes.

Supports the same parameters as OptimalTreePolicyMinimizer.

Abstract type encompassing all multi-task optimal tree learners.

Learner for training multi-task Optimal Classification Trees.

The following parameters are supported (refer to the documentation for each):

• all OptimalTreeClassifier parameters
• shared multi-task Learner parameters

Learner for training multi-task Optimal Regression Trees.

The following parameters are supported (refer to the documentation for each):

• all OptimalTreeRegressor parameters
• shared multi-task Learner parameters

refit_leaves!(lnr::OptimalTreeLearner, X::FeatureInput, y::TargetInput...;
              keyword_arguments...)

Refit the models in the leaves of the trained tree of lnr using the supplied data X and y.

See the guide to classification trees with logistic regression for an example of using this function to refit a classification tree with logistic regression in each leaf.

Keyword Arguments

• sample_weight::SampleWeightInput=nothing: the weighting to give to each data point
• criterion=:default: the scoring criterion to use when updating the leaf models
• refit_learner::Union{Nothing,Learner,GridSearch}=nothing: an optional learner or grid search that is used to fit the new model in each leaf
• prune::Bool=false: whether to prune leaves of the tree that do not contain any of the supplied data. If set to false and there are leaves that do not contain any of the supplied data, an error will be thrown.

copy_splits_and_refit_leaves!(new_lnr::OptimalTreeLearner,
                              orig_lnr::OptimalTreeLearner,
                              X::FeatureInput, y::TargetInput...;
                              keyword_arguments...)

Copy the tree split structure from orig_lnr into new_lnr and refit the models in each leaf of the tree using data X and y.

See the guide to classification trees with logistic regression for an example of using this function to convert a regression tree into a classification tree with logistic regression models in each leaf.

Supports the same keyword arguments as refit_leaves!.

prune_trees!(lnr::OptimalTreeLearner, X::FeatureInput, y::TargetInput...;
             keyword_arguments...)

Use the trained trees in lnr along with the supplied validation data X and y to determine the best value for the cp parameter and then prune the trees according to this value.

This can be useful for re-pruning trees to an appropriate level of complexity after using refit_leaves! or copy_splits_and_refit_leaves! to change the models in each leaf.

Keyword Arguments

• reselect_best_tree::Bool (required): whether to re-select the tree used by the learner. If false, the pruned version of the original tree used by the learner will be used. If true, the pruned tree with the best training performance will be used.
• sample_weight::SampleWeightInput=nothing: the weighting to give to each data point
• criterion=:default: the scoring criterion to use when updating the leaf models