DashAI.back.models.HistGradientBoostingClassifier

class HistGradientBoostingClassifier(**kwargs)[source]

Scikit-learn’s HistGradientBoostingRegressor wrapper for DashAI.

__init__(**kwargs) None[source]

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 the mean accuracy on the given test data and labels.

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.

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.