snowflake.ml.modeling.linear_model.BayesianRidge¶

class snowflake.ml.modeling.linear_model.BayesianRidge(*, max_iter=None, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, alpha_init=None, lambda_init=None, compute_score=False, fit_intercept=True, copy_X=True, verbose=False, n_iter='deprecated', input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Bayesian ridge regression For more details on this class, see sklearn.linear_model.BayesianRidge

max_iter: int, default=None

Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion. If None, it corresponds to max_iter=300.

tol: float, default=1e-3

Stop the algorithm if w has converged.

alpha_1: float, default=1e-6

Hyper-parameter: shape parameter for the Gamma distribution prior over the alpha parameter.

alpha_2: float, default=1e-6

Hyper-parameter: inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter.

lambda_1: float, default=1e-6

Hyper-parameter: shape parameter for the Gamma distribution prior over the lambda parameter.

lambda_2: float, default=1e-6

Hyper-parameter: inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter.

alpha_init: float, default=None

Initial value for alpha (precision of the noise). If not set, alpha_init is 1/Var(y).

lambda_init: float, default=None

Initial value for lambda (precision of the weights). If not set, lambda_init is 1.

compute_score: bool, default=False

If True, compute the log marginal likelihood at each iteration of the optimization.

fit_intercept: bool, default=True

Whether to calculate the intercept for this model. The intercept is not treated as a probabilistic parameter and thus has no associated variance. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

copy_X: bool, default=True

If True, X will be copied; else, it may be overwritten.

verbose: bool, default=False

Verbose mode when fitting the model.

n_iter: int

Maximum number of iterations. Should be greater than or equal to 1.

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

Methods

fit(dataset)	Fit the model For more details on this function, see sklearn.linear_model.BayesianRidge.fit
predict(dataset)	Predict using the linear model For more details on this function, see sklearn.linear_model.BayesianRidge.predict
score(dataset)	Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.BayesianRidge.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.linear_model.BayesianRidge object.

Attributes

model_signatures

Returns model signature of current class.