lale.lib.autogen.huber_regressor module

class lale.lib.autogen.huber_regressor.HuberRegressor(*, epsilon=1.35, max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True, tol=1e-05)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • epsilon (float, >=1.0 for optimizer, <=2.0 for optimizer, uniform distribution, default 1.35) – The parameter epsilon controls the number of samples that should be classified as outliers

  • max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 100) – Maximum number of iterations that scipy.optimize.fmin_l_bfgs_b should run for.

  • alpha (float, >=1e-10 for optimizer, <=1.0 for optimizer, loguniform distribution, default 0.0001) – Regularization parameter.

  • warm_start (boolean, not for optimizer, default False) – This is useful if the stored attributes of a previously used model has to be reused

  • fit_intercept (boolean, default True) – Whether or not to fit the intercept

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 1e-05) – The iteration will stop when max{|proj g_i | i = 1, ..., n} <= tol where pg_i is the i-th component of the projected gradient.

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array of items : array of items : float) – Training vector, where n_samples in the number of samples and n_features is the number of features.

  • y (array of items : float) – Target vector relative to X.

  • sample_weight (array, optional of items : float) – Weight given to each sample.

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (union type) –

Samples.

  • array of items : Any

  • or array of items : array of items : float

Returns

result – Returns predicted values.

Return type

array of items : float