`interaction` – Pairwise interaction terms only (no pure powers).#

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Operational op under axis op, sub-layer L3_A_step_op, layer l3. Standalone callable: mf.functions.interaction_terms_transform.

Function signature#

mf.functions.interaction_terms_transform(
    panel: pd.DataFrame,
) -> pd.DataFrame

Parameters#

name	type	default	constraint	description
`panel`	`pd.DataFrame`	—	—	Input panel. Each column is a variable; rows are time periods. Series is promoted to a single-column DataFrame internally.

Returns#

pd.DataFrame — scalar result.

Behavior#

Subset of polynomial degree-2 features that contains only pairwise products x_i · x_j for i ≠ j. Cheaper than full polynomial expansion when interaction structure (not non-linearity in single inputs) is the target.

When to use

Capturing predictor-pair complementarities in linear models.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: interaction

References#

macroforecast design Part 2, L3: ‘feature engineering is a DAG of typed transforms; cascade-depth bounds the longest chain at cascade_max_depth.’

interaction – Pairwise interaction terms only (no pure powers).#