ElasticMultiSegmentModel

class ElasticMultiSegmentModel(alpha: float = 1.0, l1_ratio: float = 0.5, fit_intercept: bool = True, **kwargs)[source]

Bases: etna.models.sklearn.SklearnMultiSegmentModel

Class holding sklearn.linear_model.ElasticNet for all segments.

Create instance of ElasticNet with given parameters.

Parameters

  • alpha (float) – Constant that multiplies the penalty terms. Defaults to 1.0. alpha = 0 is equivalent to an ordinary least square, solved by the LinearRegression object. For numerical reasons, using alpha = 0 with the Lasso object is not advised. Given this, you should use the LinearMultiSegmentModel object.

  • l1_ratio (float) –

    The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1.

    • For l1_ratio = 0 the penalty is an L2 penalty.

    • For l1_ratio = 1 it is an L1 penalty.

    • For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.

  • fit_intercept (bool) – Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

Inherited-members

Methods

fit(ts)

Fit model.

forecast(ts)

Make predictions.

get_model()

Get internal model that is used inside etna class.

load(path)

Load an object.

predict(ts)

Make predictions with using true values as autoregression context if possible (teacher forcing).

save(path)

Save the object.

to_dict()

Collect all information about etna object in dict.

Attributes

context_size

Context size of the model.