SklearnRegressionPerIntervalModel

class SklearnRegressionPerIntervalModel(model: Optional[sklearn.base.RegressorMixin] = None)

Bases: etna.transforms.decomposition.change_points_based.per_interval_models.base.PerIntervalModel

SklearnRegressionPerIntervalModel applies PerIntervalModel interface for sklearn-like regression models.

Init SklearnPerIntervalModel.

Parameters

model (Optional[sklearn.base.RegressorMixin]) – model with sklearn interface to use for interval processing

Inherited-members

Methods

fit(features, target, *args, **kwargs)	Fit model with given features and targets.
predict(features, *args, **kwargs)	Make prediction for given features.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.

fit(features: numpy.ndarray, target: numpy.ndarray, *args, **kwargs) → etna.transforms.decomposition.change_points_based.per_interval_models.sklearn_based.SklearnRegressionPerIntervalModel

Fit model with given features and targets.

Parameters

• features (numpy.ndarray) – features to fit model with

• target (numpy.ndarray) – targets to fit model

Returns

fitted SklearnRegressionPerIntervalModel

Return type

self

predict(features: numpy.ndarray, *args, **kwargs) → numpy.ndarray

Make prediction for given features.

Parameters

features (numpy.ndarray) – features to make prediction for

Returns

model’s prediction for given features

Return type

prediction