evalml.pipelines.components.LightGBMRegressor

class evalml.pipelines.components.LightGBMRegressor(boosting_type='gbdt', learning_rate=0.1, n_estimators=20, max_depth=0, num_leaves=31, min_child_samples=20, n_jobs=- 1, random_state=None, random_seed=0, bagging_fraction=0.9, bagging_freq=0, **kwargs)

LightGBM Regressor

name = 'LightGBM Regressor'

model_family = 'lightgbm'

supported_problem_types = [<ProblemTypes.REGRESSION: 'regression'>]

hyperparameter_ranges = {'bagging_fraction': Real(low=1e-06, high=1, prior='uniform', transform='identity'), 'bagging_freq': Integer(low=0, high=1, prior='uniform', transform='identity'), 'boosting_type': ['gbdt', 'dart', 'goss', 'rf'], 'learning_rate': Real(low=1e-06, high=1, prior='uniform', transform='identity'), 'max_depth': Integer(low=0, high=10, prior='uniform', transform='identity'), 'min_child_samples': Integer(low=1, high=100, prior='uniform', transform='identity'), 'n_estimators': Integer(low=10, high=100, prior='uniform', transform='identity'), 'num_leaves': Integer(low=2, high=100, prior='uniform', transform='identity')}

default_parameters = {'bagging_fraction': 0.9, 'bagging_freq': 0, 'boosting_type': 'gbdt', 'learning_rate': 0.1, 'max_depth': 0, 'min_child_samples': 20, 'n_estimators': 20, 'n_jobs': -1, 'num_leaves': 31}

predict_uses_y = False

Instance attributes

SEED_MAX
SEED_MIN
feature_importance	Returns importance associated with each feature.
needs_fitting
parameters	Returns the parameters which were used to initialize the component

Methods:

__init__	Initialize self.
clone	Constructs a new component with the same parameters and random state.
describe	Describe a component and its parameters
fit	Fits component to data
load	Loads component at file path
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves component at file path

Class Inheritance

evalml.pipelines.components.DecisionTreeRegressor.save evalml.pipelines.components.LightGBMRegressor.__init__