svr_rbf – Support vector regression with RBF kernel.#

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

Function signature#

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

Parameters#

name

type

default

constraint

description

X

`np.ndarray

pd.DataFrame`

—

—

y

`np.ndarray

pd.Series`

—

—

Returns#

SVRRBFFitResult — frozen dataclass with fit results.

Attribute

Type

Description

.C

float

Regularisation parameter used.

.gamma

`str

float`

.n_support_vectors

int

Number of support vectors.

.predict(X)

np.ndarray

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

.summary()

str

Table: C, gamma, support vector count.

Behavior#

Non-linear regression via kernel trick. Slow on large panels (O(n³)).

Configures the family axis on L4_A_model_selection (layer l4); the svr_rbf value is materialised in the recipe’s fixed_axes block under that sub-layer.

When to use

Small / medium-dim non-linear regression; kernel-method ablations.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: svr_rbf

References#

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