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, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶
Bases:
BaseTransformerBayesian ridge regression For more details on this class, see sklearn.linear_model.BayesianRidge
- 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, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.
- label_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that contain labels. Label columns must be specified with this parameter during initialization or with the set_label_cols method before fitting.
- 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 must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.
- sample_weight_col: Optional[str]
A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.
- passthrough_cols: Optional[Union[str, List[str]]]
A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.
- drop_input_cols: Optional[bool], default=False
If set, the response of predict(), transform() methods will not contain input columns.
- 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.
Methods
fit(dataset)Fit the model For more details on this function, see sklearn.linear_model.BayesianRidge.fit
get_input_cols()Input columns getter.
get_label_cols()Label column getter.
get_output_cols()Output columns getter.
get_params([deep])Get parameters for this transformer.
get_passthrough_cols()Passthrough columns getter.
get_sample_weight_col()Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])Get sklearn keyword arguments.
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_drop_input_cols([drop_input_cols])set_input_cols(input_cols)Input columns setter.
set_label_cols(label_cols)Label column setter.
set_output_cols(output_cols)Output columns setter.
set_params(**params)Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)Passthrough columns setter.
set_sample_weight_col(sample_weight_col)Sample weight column setter.
to_sklearn()Get sklearn.linear_model.BayesianRidge object.
Attributes
model_signaturesReturns model signature of current class.