snowflake.ml.modeling.preprocessing.Binarizer¶

class snowflake.ml.modeling.preprocessing.Binarizer(*, threshold: float = 0.0, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False)¶

Bases: BaseTransformer

Binarizes data (sets feature values to 0 or 1) according to the given threshold.

Values must be of float type. Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. The default threshold of 0.0 maps only positive values to 1.

For more details on what this transformer does, see sklearn.preprocessing.Binarizer.

Args:

threshold: float, default=0.0

Feature values below or equal to this are replaced by 0, above it by 1. Default values is 0.0.

input_cols: Optional[Union[str, Iterable[str]]], default=None

The name(s) of one or more columns in a DataFrame containing a feature to be binarized.

output_cols: Optional[Union[str, Iterable[str]]], default=None

The name(s) of one or more columns in a DataFrame in which results will be stored. The number of columns specified must match the number of input columns.

passthrough_cols: Optional[Union[str, Iterable[str]]], default=None

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.

drop_input_cols: Optional[bool], default=False

Remove input columns from output if set True. False by default.

Methods

fit(dataset)	This is a stateless transformer, so there is nothing to 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.
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_lightgbm()
to_sklearn()
to_xgboost()
transform(dataset)	Binarize the data.