`scale` – Standardise to zero mean and unit variance.#

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

Function signature#

mf.functions.scale_transform(
    panel: pd.DataFrame,
    method: str enum {"zscore", "standard", "standardize", "robust", "minmax"},
) -> 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.
`method`	`str enum {"zscore", "standard", "standardize", "robust", "minmax"}`	`'zscore'`	—	Scaling method. “zscore”/“standard”/“standardize” for zero-mean/unit-std; “robust” for median/IQR; “minmax” for [0, 1] range.

Returns#

pd.DataFrame — scalar result.

Behavior#

Computes (y - μ) / σ over the temporal_rule window (expanding_window_per_origin by default to avoid look-ahead). Required pre-step for distance-based learners (kNN, SVM, NN); ridge/lasso also benefit when columns are on different scales.

When to use

Pre-conditioning for distance-based or regularised learners; mandatory for SVM/NN.

When NOT to use

Tree-based models (RF/XGBoost/LightGBM) – scale-invariant by construction.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: scale

References#

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

scale – Standardise to zero mean and unit variance.#