snowflake.ml.modeling.feature_selection.SelectFdr¶
- class snowflake.ml.modeling.feature_selection.SelectFdr(*, score_func=<function f_classif>, alpha=0.05, input_cols: ~typing.Optional[~typing.Union[str, ~typing.Iterable[str]]] = None, output_cols: ~typing.Optional[~typing.Union[str, ~typing.Iterable[str]]] = None, label_cols: ~typing.Optional[~typing.Union[str, ~typing.Iterable[str]]] = None, drop_input_cols: ~typing.Optional[bool] = False, sample_weight_col: ~typing.Optional[str] = None)¶
Bases:
BaseTransformerFilter: Select the p-values for an estimated false discovery rate For more details on this class, see sklearn.feature_selection.SelectFdr
- score_func: callable, default=f_classif
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). Default is f_classif (see below “See Also”). The default function only works with classification tasks.
- alpha: float, default=5e-2
The highest uncorrected p-value for features to keep.
- 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)Run score function on (X, y) and get the appropriate features For more details on this function, see sklearn.feature_selection.SelectFdr.fit
score(dataset)Method not supported for this class.
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.feature_selection.SelectFdr object.
transform(dataset)Reduce X to the selected features For more details on this function, see sklearn.feature_selection.SelectFdr.transform
Attributes
model_signaturesReturns model signature of current class.