`elastic_net` – Elastic net (L1 + L2 hybrid).#

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Operational op under axis family, sub-layer L4_A_model_selection, layer l4. Standalone callable: mf.functions.elastic_net_fit.

Function signature#

mf.functions.elastic_net_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
    *,
    alpha: float = 1.0,
    l1_ratio: float = 0.5,
    max_iter: int = 20000,
) -> ElasticNetFitResult

Parameters#

name	type	default	constraint	description
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—
`alpha`	`float`	`1.0`	>=0	Overall regularisation strength.
`l1_ratio`	`float`	`0.5`	in [0.0, 1.0]	L1/L2 mixing parameter. 0 = pure ridge, 1 = pure lasso.
`max_iter`	`int`	`20000`	>=1	Maximum number of coordinate descent iterations.

Returns#

ElasticNetFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.coef_`	`np.ndarray`	Fitted coefficient vector, shape (n_features,).
`.intercept_`	`float`	Fitted intercept scalar.
`.alpha`	`float`	Regularisation strength used.
`.l1_ratio`	`float`	L1/L2 mixing parameter used.
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Human-readable text table of fit results.

Behavior#

Combines ridge and lasso penalties via params.l1_ratio (0 = ridge, 1 = lasso). Useful when predictors are correlated and pure lasso struggles with the selection.

When to use

Correlated predictor blocks where lasso alone gives unstable selection.

In recipe context#

Set params.family = "elastic_net" in the relevant layer to activate this op within a recipe:

# Layer L4 recipe fragment
params:
  family: elastic_net

References#

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Zou & Hastie (2005) ‘Regularization and variable selection via the elastic net’, JRSS-B 67(2).

elastic_net – Elastic net (L1 + L2 hybrid).#