mlrl.boosting package

Submodules

mlrl.boosting.boosting_learners module

Author: Michael Rapp (michael.rapp.ml@gmail.com)

Provides scikit-learn implementations of boosting algorithms.

class mlrl.boosting.boosting_learners.Boomer(random_state: int = 1, feature_format: str = 'auto', label_format: str = 'auto', prediction_format: str = 'auto', rule_model_assemblage: Optional[str] = None, rule_induction: Optional[str] = None, max_rules: Optional[int] = None, time_limit: Optional[int] = None, early_stopping: Optional[str] = None, head_type: Optional[str] = None, loss: Optional[str] = None, predictor: Optional[str] = 'auto', label_sampling: Optional[str] = None, instance_sampling: Optional[str] = None, feature_sampling: Optional[str] = None, holdout: Optional[str] = None, feature_binning: Optional[str] = None, label_binning: Optional[str] = None, pruning: Optional[str] = None, shrinkage: Optional[float] = 0.3, l1_regularization_weight: Optional[float] = None, l2_regularization_weight: Optional[float] = None, parallel_rule_refinement: Optional[str] = None, parallel_statistic_update: Optional[str] = None, parallel_prediction: Optional[str] = None)

Bases: mlrl.common.rule_learners.MLRuleLearner, sklearn.base.ClassifierMixin

A scikit-multilearn implementation of “BOOMER”, an algorithm for learning gradient boosted multi-label classification rules.

get_name() → str

Returns a human-readable name that allows to identify the configuration used by the classifier or ranker.

Returns

The name of the classifier or ranker

Module contents