`huber` – Huber regression (robust to outliers).#

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

Function signature#

mf.functions.huber_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
    *,
    epsilon: float = 1.35,
    max_iter: int = 1000,
) -> HuberFitResult

Parameters#

name	type	default	constraint	description
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—
`epsilon`	`float`	`1.35`	>1.0	Huber loss transition point. Residuals with
`max_iter`	`int`	`1000`	>=1	Maximum number of LBFGS iterations.

Returns#

HuberFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.coef_`	`np.ndarray`	Fitted coefficient vector, shape (n_features,).
`.intercept_`	`float`	Fitted intercept scalar.
`.epsilon`	`float`	Huber loss transition point used.
`.scale_`	`float`	Robust scale estimate from the fitted model.
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Human-readable text table of fit results.

Behavior#

Replaces squared loss with the Huber loss: quadratic for small residuals, linear for large ones. Down-weights outliers without removing them. params.epsilon (default 1.35) sets the transition point.

When to use

Series with sporadic outliers that aren’t worth flagging in L2.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: huber

References#

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Huber (1964) ‘Robust Estimation of a Location Parameter’, Annals of Mathematical Statistics 35(1).

huber – Huber regression (robust to outliers).#