mars – Multivariate Adaptive Regression Splines (Friedman 1991).#

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

Function signature#

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

Parameters#

name

type

default

constraint

description

X

`np.ndarray

pd.DataFrame`

—

—

y

`np.ndarray

pd.Series`

—

—

Returns#

MARSFitResult — frozen dataclass with fit results.

Attribute

Type

Description

.n_terms

int

Number of MARS basis terms.

.n_features_in_

int

Number of input features.

.predict(X)

np.ndarray

Predictions for new data X, shape (n_samples,).

.summary()

str

Table: term count and feature count.

Behavior#

Greedy forward / backward selection of piecewise-linear hinge basis functions max(0, x - c) and their products. Atomic primitive – sklearn does not provide a MARS implementation. Runtime wraps pyearth as an optional dep; install via pip install macroforecast[mars]. Required as the base learner for the Coulombe (2024) ‘MARSquake’ recipe (bagging(base_family=mars, ...)).

Operational from v0.9.0; raises NotImplementedError with an install hint when pyearth is not present (mirrors the xgboost / lightgbm / catboost optional-dep error pattern).

When to use

Non-linear regression with interpretable basis functions; MARSquake recipe base learner.

When NOT to use

Without [mars] extra installed – raises a clear NotImplementedError.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: mars

References#

  • macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

  • Friedman (1991) ‘Multivariate Adaptive Regression Splines’, Annals of Statistics 19(1).