`lasso` – Lasso regression (L1-regularised OLS).#

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

Function signature#

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

Parameters#

name	type	default	constraint	description
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—
`alpha`	`float`	`1.0`	>=0	L1 regularisation strength. Larger values force more coefficients to exactly zero.
`max_iter`	`int`	`20000`	>=1	Maximum number of coordinate descent iterations.

Returns#

LassoFitResult — 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.
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Human-readable text table of fit results.

Behavior#

Iterative coordinate descent: minimises ||y - Xβ||² + α||β||₁. Forces a subset of coefficients to exactly zero, yielding a sparse solution. Uses sklearn’s Lasso with max_iter=20000 for stability.

When to use

Variable selection; sparse forecasts on high-dimensional panels.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: lasso

References#

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Tibshirani (1996) ‘Regression Shrinkage and Selection via the Lasso’, JRSS-B 58(1).

lasso – Lasso regression (L1-regularised OLS).#