`mlp` – Multi-layer perceptron (sklearn).#

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

Function signature#

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

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

MLPFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.n_params`	`int`	Total number of trainable parameters (weights + biases).
`.n_features_in_`	`int`	Number of input features seen during fit.
`.hidden_layer_sizes`	`tuple`	Tuple of hidden layer widths, e.g. (32, 16).
`.epochs_used`	`int`	Number of optimiser iterations completed.
`.final_loss`	`float`	Training MSE at the end of fitting.
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Arch metadata table: model_type, hidden_layer_sizes, n_features, n_params, epochs_used, final_loss.

Feed-forward NN with ReLU activations. params.hidden_layer_sizes controls the architecture.

v0.9 sub-axes (apply equally to mlp / lstm / gru / transformer):

params.architecture – network topology. standard (default) is the standard feed-forward / sequence variant. hemisphere (future) implements Coulombe / Frenette / Klieber (2025 JAE) HNN with separate mean / variance hemispheres joined by a constraint loss.
params.loss – objective. mse (default), quantile (operational via forecast_object=quantile), volatility_emphasis (future, HNN constraint loss).

When to use

Non-linear regression baselines; ablations against deep NN.

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

# Layer L4 recipe fragment
params:
  family: mlp

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