`lightgbm` – LightGBM gradient-boosted trees (optional dependency).#

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

Function signature#

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

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

LightGBMFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.feature_importances_`	`np.ndarray`	Feature importances (split count) from LightGBM, shape (n_features,).
`.n_estimators_used`	`int`	Number of boosting rounds (= 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.

Requires pip install macroforecast[lightgbm]. Leaf-wise tree growth; fast on wide / categorical-heavy panels.

When to use

Wide categorical panels; production sweeps where lightgbm’s speed matters.

When NOT to use

Lightweight installs (no extra installed) – raises ImportError.

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

# Layer L4 recipe fragment
params:
  family: lightgbm

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Ke et al. (2017) ‘LightGBM: A Highly Efficient Gradient Boosting Decision Tree’, NeurIPS.