DashAI.back.models.HistGradientBoostingClassifier

class HistGradientBoostingClassifier(**kwargs)

Scikit-learn’s HistGradientBoostingRegressor wrapper for DashAI.

__init__(**kwargs) → None

Methods

__init__(**kwargs)
decision_function(X)	Compute the decision function of X.
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.
predict_proba(X)	Predict class probabilities for X.
save(filename)	Save the model in the specified path.
score(X, y[, sample_weight])	Return accuracy on provided data and labels.
set_fit_request(*[, x_train, y_train])	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_decision_function(X)	Compute decision function of X for each iteration.
staged_predict(X)	Predict classes at each iteration.
staged_predict_proba(X)	Predict class probabilities at each iteration.
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
n_iter_	Number of iterations of the boosting process.