ElasticPerSegmentModel¶

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

Notes

Target components are formed as the terms from linear regression formula.

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 LinearPerSegmentModel 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[, return_components])	Make predictions.
`get_model`()	Get internal models that are used inside etna class.
`load`(path)	Load an object.
`params_to_tune`()	Get default grid for tuning hyperparameters.
`predict`(ts[, return_components])	Make predictions with using true values as autoregression context if possible (teacher forcing).
`save`(path)	Save the object.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

Attributes

context_size

Context size of the model.

Get default grid for tuning hyperparameters.