DashAI.back.models.GradientBoostingR

class GradientBoostingR(**kwargs)

Scikit-learn’s Ridge Regression wrapper for DashAI.

__init__(**kwargs) → None

Methods

__init__(**kwargs)
apply(X)	Apply trees in the ensemble to X, return leaf indices.
calculate_metrics([split, level, log_index, ...])	Calculate and save metrics for a given data split and level.
fit(X, y[, sample_weight, monitor])	Fit the gradient boosting model.
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.
prepare_dataset(dataset[, is_fit])	Apply the model transformations to the dataset.
prepare_output(dataset[, is_fit])	Prepare output targets using Label encoding.
save(filename)	Save the model in the specified path.
score(X, y[, sample_weight])	Return coefficient of determination on test data.
set_fit_request(*[, monitor, 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_predict_request(*[, x_pred])	Configure whether metadata should be requested to be passed to the predict method.
set_score_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the score method.
staged_predict(X)	Predict regression target at each stage for X.
train(x_train, y_train[, x_validation, ...])	Train the model with the provided data.
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

CATEGORICAL_ENCODING
COLOR
COMPATIBLE_COMPONENTS
DISPLAY_NAME
TYPE
feature_importances_	The impurity-based feature importances.