lale.lib.autogen.ard_regression module¶
- class lale.lib.autogen.ard_regression.ARDRegression(*, n_iter='deprecated', tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, compute_score=False, threshold_lambda=10000.0, fit_intercept=True, copy_X=True, verbose=False, max_iter=None)¶
Bases:
PlannedIndividualOp
Combined schema for expected data and hyperparameters.
This documentation is auto-generated from JSON schemas.
- Parameters
n_iter (union type, default 'deprecated') –
Deprecated. Use max_iter instead.
integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution, default 300
Maximum number of iterations
or ‘deprecated’
tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.001) – Stop the algorithm if w has converged
alpha_1 (float, not for optimizer, default 1e-06) – Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter
alpha_2 (float, not for optimizer, default 1e-06) – Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter
lambda_1 (float, not for optimizer, default 1e-06) – Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter
lambda_2 (float, not for optimizer, default 1e-06) – Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter
compute_score (boolean, default False) – If True, compute the objective function at each step of the model
threshold_lambda (float, not for optimizer, default 10000.0) – threshold for removing (pruning) weights with high precision from the computation
fit_intercept (boolean, default True) – whether to calculate the intercept for this model
copy_X (boolean, default True) – If True, X will be copied; else, it may be overwritten.
verbose (boolean, not for optimizer, default False) – Verbose mode when fitting the model.
max_iter (union type, optional, not for optimizer, default None) –
Maximum number of iterations
integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution
or None
Corresponds to 300
- 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 values (integers)
- 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 (array of items : array of items : float) – Samples.
return_std (union type, optional, default None) –
Whether to return the standard deviation of posterior prediction.
boolean
or None
- Returns
result – Predict using the linear model.
- Return type
Any