`glmboost` – Componentwise L2-boosting with linear base learners.#

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

Function signature#

mf.functions.glmboost_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
    *,
    n_iter: int = 100,
    learning_rate: float = 0.1,
) -> GLMBoostFitResult

Parameters#

name	type	default	constraint	description
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—
`n_iter`	`int`	`100`	>=1	Number of boosting iterations. More iterations = finer coefficient path.
`learning_rate`	`float`	`0.1`	>0	Shrinkage factor applied to each coefficient update. Smaller = slower convergence, more regularisation.

Returns#

GLMBoostFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.coef_`	`np.ndarray`	Fitted coefficient vector, shape (n_features,).
`.intercept_`	`float`	Fitted intercept scalar (initialised to mean(y)).
`.n_iter`	`int`	Number of boosting iterations used.
`.learning_rate`	`float`	Shrinkage factor used.
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Human-readable text table of fit results.

Behavior#

Bühlmann-Hothorn (2007) componentwise boosting: at each iteration picks the predictor most correlated with the residual and updates only its coefficient. Approximates lasso with a boosting interpretation.

When to use

Transparent feature-selection pathways; alternative to lasso.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: glmboost

References#

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Bühlmann & Hothorn (2007) ‘Boosting algorithms: Regularization, prediction and model fitting’, Statistical Science 22(4).

glmboost – Componentwise L2-boosting with linear base learners.#