LearningTrees

Decision-tree models. Trainers in the scikit-learn Trees family, behind Learning's typed contract. Each requires a clean training set (``independent_target`` and no missing values) and stamps the model with the dataset's feature/class count. ``open LearningTrees`` brings the core types in scope too.
Imports
open LearningCore;
Table of Contents

Functions

decision_tree_classifier

def decision_tree_classifier (ds: {d : Dataset | independent_target d = True && has_missing d = False}) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

extra_tree_classifier

def extra_tree_classifier (ds: {d : Dataset | independent_target d = True && has_missing d = False}) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

decision_tree_regressor

def decision_tree_regressor (ds: {d : Dataset | independent_target d = True && has_missing d = False}) : {m : SklearnRegressor | reg_features m = ds_features ds}

extra_tree_regressor

def extra_tree_regressor (ds: {d : Dataset | independent_target d = True && has_missing d = False}) : {m : SklearnRegressor | reg_features m = ds_features ds}