guide.GuideForestClassifier

class guide.GuideForestClassifier(*, kind='forest', n_estimators=100, max_samples=1.0, max_features=None, max_depth=10, min_samples_leaf=2, min_gain=1e-12, interactions=True, linear_splits=False, significance_level=0.1, max_split_candidates=100, pruning='none', cv_folds=10, cv_standard_errors=0.25, ccp_alpha=0.0, max_leaves=None, class_priors=None, class_costs=None, pruning_loss='misclassification', categorical_features=None, feature_roles=None, periodic_features=None, missing_flags=None, n_jobs=None, random_state=0)

Ensemble of GUIDE classification trees fitted to bootstrap samples.

Probabilities are averaged over trees, and the predicted class minimizes expected cost. max_features defaults to the square root of the number of splitting features. The remaining parameters are those of GuideForestRegressor and GuideClassifier.

__init__(*, kind='forest', n_estimators=100, max_samples=1.0, max_features=None, max_depth=10, min_samples_leaf=2, min_gain=1e-12, interactions=True, linear_splits=False, significance_level=0.1, max_split_candidates=100, pruning='none', cv_folds=10, cv_standard_errors=0.25, ccp_alpha=0.0, max_leaves=None, class_priors=None, class_costs=None, pruning_loss='misclassification', categorical_features=None, feature_roles=None, periodic_features=None, missing_flags=None, n_jobs=None, random_state=0)

Methods

__init__(*[, kind, n_estimators, ...])
apply(X)	Terminal node ids, shape (n_samples, n_estimators).
fit(X, y[, sample_weight, validation_data])	Fit the model.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predicted class labels (cost-minimizing when costs are given).
predict_proba(X)	Class probabilities in the order of classes_.
score(X, y[, sample_weight])	Return accuracy on provided data and labels.
set_fit_request(*[, sample_weight, ...])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the score method.
to_dot()	The fitted model in Graphviz DOT format.
to_json()	The fitted model in the format saved by the guide-rs CLI.
to_text()	The fitted model as indented text.

Attributes

oob_loss_

Mean out-of-bag loss, or None if no row is out of bag.