`gradient_boosting` – Gradient-boosted regression trees (sklearn).#

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

Function signature#

mf.functions.gradient_boosting_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
) -> GradientBoostingFitResult

Parameters#

name	type	default	constraint	description
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—

Returns#

GradientBoostingFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.feature_importances_`	`np.ndarray`	Feature importances from the GBM, shape (n_features,). Sums to 1.0.
`.n_estimators_used`	`int`	Number of boosting iterations (= n_estimators parameter).
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Human-readable table of fit results including top-3 feature importances.

Behavior#

Sklearn GradientBoostingRegressor. Sequential boosting with shallow trees. params.n_estimators (default 200) and params.learning_rate (default 0.05) trade variance for bias.

When to use

Default boosted baseline when xgboost / lightgbm are unavailable.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: gradient_boosting

References#

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Friedman (2001) ‘Greedy function approximation: A gradient boosting machine’, Annals of Statistics 29(5).

gradient_boosting – Gradient-boosted regression trees (sklearn).#