sklearn_quantile.RandomForestMaximumRegressor
- class sklearn_quantile.RandomForestMaximumRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)
A random forest regressor predicting conditional maxima
Implementation is equivalent to Random Forest Quantile Regressor, but calculation is much faster. For other quantiles revert to the original predictor.
- __init__(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None, monotonic_cst=None)
Methods
__init__([n_estimators, criterion, ...])apply(X)Apply trees in the forest to X, return leaf indices.
decision_path(X)Return the decision path in the forest.
fit(X, y[, sample_weight])Build a forest from the training set (X, y).
get_metadata_routing()Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
predict(X)predition, based on the predict function of ForestRegressor
score(X, y)Mean pinball loss for the quantile regressors.
set_fit_request(*[, sample_weight])Request metadata passed to the
fitmethod.set_params(**params)Set the parameters of this estimator.
set_score_request(*[, sample_weight])Request metadata passed to the
scoremethod.validate_quantiles()Validate the quantiles inserted in the quantile regressor
Attributes
estimators_samples_The subset of drawn samples for each base estimator.
feature_importances_The impurity-based feature importances.