snowflake.ml.modeling.multiclass.OneVsRestClassifier¶

class snowflake.ml.modeling.multiclass.OneVsRestClassifier(*, estimator, n_jobs=None, verbose=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)¶

Bases: BaseTransformer

One-vs-the-rest (OvR) multiclass strategy For more details on this class, see sklearn.multiclass.OneVsRestClassifier

estimator: estimator object

A regressor or a classifier that implements fit. When a classifier is passed, decision_function will be used in priority and it will fallback to predict_proba if it is not available. When a regressor is passed, predict is used.

n_jobs: int, default=None

The number of jobs to use for the computation: the n_classes one-vs-rest problems are computed in parallel.

None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

verbose: int, default=0

The verbosity level, if non zero, progress messages are printed. Below 50, the output is sent to stderr. Otherwise, the output is sent to stdout. The frequency of the messages increases with the verbosity level, reporting all iterations at 10. See joblib.Parallel for more details.

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

decision_function(dataset[, output_cols_prefix])	Decision function for the OneVsRestClassifier For more details on this function, see sklearn.multiclass.OneVsRestClassifier.decision_function
fit(dataset)	Fit underlying estimators For more details on this function, see sklearn.multiclass.OneVsRestClassifier.fit
predict(dataset)	Predict multi-class targets using underlying estimators For more details on this function, see sklearn.multiclass.OneVsRestClassifier.predict
predict_proba(dataset[, output_cols_prefix])	Probability estimates For more details on this function, see sklearn.multiclass.OneVsRestClassifier.predict_proba
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.multiclass.OneVsRestClassifier.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.multiclass.OneVsRestClassifier object.

Attributes

model_signatures

Returns model signature of current class.