snowflake.ml.modeling.linear_model.ARDRegression¶
- class snowflake.ml.modeling.linear_model.ARDRegression(*, max_iter=None, 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, 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:
BaseTransformerBayesian ARD regression For more details on this class, see sklearn.linear_model.ARDRegression
- max_iter: int, default=None
Maximum number of iterations. 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.
- compute_score: bool, default=False
If True, compute the objective function at each step of the model.
- threshold_lambda: float, default=10 000
Threshold for removing (pruning) weights with high precision from the computation.
- fit_intercept: bool, default=True
Whether to calculate the intercept for this model. 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.
- 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 according to the given training data and parameters For more details on this function, see sklearn.linear_model.ARDRegression.fit
predict(dataset)Predict using the linear model For more details on this function, see sklearn.linear_model.ARDRegression.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.ARDRegression.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.linear_model.ARDRegression object.
Attributes
model_signaturesReturns model signature of current class.