DashAI.back.models.RandomForestRegression

class RandomForestRegression(**kwargs)

Scikit-learn’s Ridge Regression wrapper for DashAI.

__init__(**kwargs) → None

Methods

__init__(**kwargs)
apply(X)	Apply trees in the forest to X, return leaf indices.
decision_path(X)	Return the decision path in the forest.
fit(x_train, y_train)	Fit the estimator.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
get_schema()	Generates the component related Json Schema.
load(filename)	Load the model of the specified path.
predict(x_pred)	Make a prediction with the model.
save(filename)	Save the model in the specified path.
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_fit_request(*[, x_train, y_train])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_predict_request(*[, x_pred])	Request metadata passed to the predict method.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.
validate_and_transform(raw_data)	It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Attributes

COMPATIBLE_COMPONENTS
TYPE
base_estimator_	Estimator used to grow the ensemble.
feature_importances_	The impurity-based feature importances.