snowflake.ml.modeling.compose.TransformedTargetRegressor¶
- class snowflake.ml.modeling.compose.TransformedTargetRegressor(*, regressor=None, transformer=None, func=None, inverse_func=None, check_inverse=True, 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:
BaseTransformerMeta-estimator to regress on a transformed target For more details on this class, see sklearn.compose.TransformedTargetRegressor
- regressor: object, default=None
Regressor object such as derived from
RegressorMixin. This regressor will automatically be cloned each time prior to fitting. If regressor is None,LinearRegressionis created and used.- transformer: object, default=None
Estimator object such as derived from
TransformerMixin. Cannot be set at the same time as func and inverse_func. If transformer is None as well as func and inverse_func, the transformer will be an identity transformer. Note that the transformer will be cloned during fitting. Also, the transformer is restricting y to be a numpy array.- func: function, default=None
Function to apply to y before passing to
fit(). Cannot be set at the same time as transformer. The function needs to return a 2-dimensional array. If func is None, the function used will be the identity function.- inverse_func: function, default=None
Function to apply to the prediction of the regressor. Cannot be set at the same time as transformer. The function needs to return a 2-dimensional array. The inverse function is used to return predictions to the same space of the original training labels.
- check_inverse: bool, default=True
Whether to check that transform followed by inverse_transform or func followed by inverse_func leads to the original targets.
- 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 For more details on this function, see sklearn.compose.TransformedTargetRegressor.fit
predict(dataset)Predict using the base regressor, applying inverse For more details on this function, see sklearn.compose.TransformedTargetRegressor.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.compose.TransformedTargetRegressor.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.compose.TransformedTargetRegressor object.
Attributes
model_signaturesReturns model signature of current class.