ElasticMultiSegmentModel

class ElasticMultiSegmentModel(alpha: float = 1.0, l1_ratio: float = 0.5, fit_intercept: bool = True, normalize: bool = False, **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).

  • normalize (bool) – This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm.

Inherited-members

Methods

fit(ts)

Fit model.

forecast(ts)

Make predictions.

get_model()

Get internal model that is used inside etna class.