snowflake.ml.modeling.feature_selection.VarianceThresholdΒΆ
- class snowflake.ml.modeling.feature_selection.VarianceThreshold(*, threshold=0.0, 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)ΒΆ
Bases:
BaseTransformerFeature selector that removes all low-variance features For more details on this class, see sklearn.feature_selection.VarianceThreshold
- threshold: float, default=0
Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples.
- 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 estimatorβs predict() method, but output_cols must be set explicitly for transformers.
- sample_weight_col: Optional[str]
A string representing the column name containing the examplesβ 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)Learn empirical variances from X For more details on this function, see sklearn.feature_selection.VarianceThreshold.fit
score(dataset)Method not supported for this class.
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.feature_selection.VarianceThreshold object.
transform(dataset)Reduce X to the selected features For more details on this function, see sklearn.feature_selection.VarianceThreshold.transform
Attributes
model_signaturesReturns model signature of current class.