snowflake.ml.modeling.ensemble.VotingClassifier¶

class snowflake.ml.modeling.ensemble.VotingClassifier(*, estimators, voting='hard', weights=None, n_jobs=None, flatten_transform=True, verbose=False, 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

Soft Voting/Majority Rule classifier for unfitted estimators For more details on this class, see sklearn.ensemble.VotingClassifier

estimators: list of (str, estimator) tuples

Invoking the fit method on the VotingClassifier will fit clones of those original estimators that will be stored in the class attribute self.estimators_. An estimator can be set to 'drop' using set_params().

voting: {‘hard’, ‘soft’}, default=’hard’

If ‘hard’, uses predicted class labels for majority rule voting. Else if ‘soft’, predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers.

weights: array-like of shape (n_classifiers,), default=None

Sequence of weights (float or int) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights if None.

n_jobs: int, default=None

The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

flatten_transform: bool, default=True

Affects shape of transform output only when voting=’soft’ If voting=’soft’ and flatten_transform=True, transform method returns matrix with shape (n_samples, n_classifiers * n_classes). If flatten_transform=False, it returns (n_classifiers, n_samples, n_classes).

verbose: bool, default=False

If True, the time elapsed while fitting will be printed as it is completed.

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 estimators For more details on this function, see sklearn.ensemble.VotingClassifier.fit
predict(dataset)	Predict class labels for X For more details on this function, see sklearn.ensemble.VotingClassifier.predict
predict_proba(dataset[, output_cols_prefix])	Compute probabilities of possible outcomes for samples in X For more details on this function, see sklearn.ensemble.VotingClassifier.predict_proba
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.ensemble.VotingClassifier.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.ensemble.VotingClassifier object.
transform(dataset)	Return class labels or probabilities for X for each estimator For more details on this function, see sklearn.ensemble.VotingClassifier.transform

Attributes

model_signatures

Returns model signature of current class.