LearningEnsembles

Ensemble models (forests, boosting, bagging). Trainers in the scikit-learn Ensembles 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 LearningEnsembles`` brings the core types in scope too.
Imports
open LearningCore;
Table of Contents

Functions

random_forest_classifier

def random_forest_classifier (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

random_forest_classifier_n

def random_forest_classifier_n (n : Int | n > 0) (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

gradient_boosting_classifier

def gradient_boosting_classifier (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

adaboost_classifier

def adaboost_classifier (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

bagging_classifier

def bagging_classifier (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

extra_trees_classifier

def extra_trees_classifier (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

hist_gradient_boosting_classifier

def hist_gradient_boosting_classifier (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

random_forest_regressor

def random_forest_regressor (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}

random_forest_regressor_n

def random_forest_regressor_n (n : Int | n > 0) (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}

gradient_boosting_regressor

def gradient_boosting_regressor (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}

adaboost_regressor

def adaboost_regressor (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}

bagging_regressor

def bagging_regressor (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}

extra_trees_regressor

def extra_trees_regressor (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}

hist_gradient_boosting_regressor

def hist_gradient_boosting_regressor (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}