snowflake.ml.modeling.ensemble.AdaBoostClassifier¶

class snowflake.ml.modeling.ensemble.AdaBoostClassifier(*, estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None, base_estimator='deprecated', 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

An AdaBoost classifier For more details on this class, see sklearn.ensemble.AdaBoostClassifier

estimator: object, default=None

The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes_ and n_classes_ attributes. If None, then the base estimator is DecisionTreeClassifier initialized with max_depth=1.

n_estimators: int, default=50

The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early. Values must be in the range [1, inf).

learning_rate: float, default=1.0

Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the learning_rate and n_estimators parameters. Values must be in the range (0.0, inf).

algorithm: {‘SAMME’, ‘SAMME.R’}, default=’SAMME.R’

If ‘SAMME.R’ then use the SAMME.R real boosting algorithm. estimator must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.

random_state: int, RandomState instance or None, default=None

Controls the random seed given at each estimator at each boosting iteration. Thus, it is only used when estimator exposes a random_state. Pass an int for reproducible output across multiple function calls. See Glossary.

base_estimator: object, default=None

The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes_ and n_classes_ attributes. If None, then the base estimator is DecisionTreeClassifier initialized with max_depth=1.

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])	Compute the decision function of X For more details on this function, see sklearn.ensemble.AdaBoostClassifier.decision_function
fit(dataset)	Build a boosted classifier/regressor from the training set (X, y) For more details on this function, see sklearn.ensemble.AdaBoostClassifier.fit
predict(dataset)	Predict classes for X For more details on this function, see sklearn.ensemble.AdaBoostClassifier.predict
predict_log_proba(dataset[, output_cols_prefix])	Predict class probabilities for X For more details on this function, see sklearn.ensemble.AdaBoostClassifier.predict_proba
predict_proba(dataset[, output_cols_prefix])	Predict class probabilities for X For more details on this function, see sklearn.ensemble.AdaBoostClassifier.predict_proba
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.ensemble.AdaBoostClassifier.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.ensemble.AdaBoostClassifier object.

Attributes

model_signatures

Returns model signature of current class.