search_algorithm

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Axis search_algorithm on sub-layer L4_D_tuning (layer l4).

Sub-layer

L4_D_tuning

Axis metadata

  • Default: 'none'

  • Sweepable: True

  • Status: operational

Operational status summary

  • Operational: 6 option(s)

  • Future: 0 option(s)

Options

none – operational

No tuning; use the params block as-is.

Default. The recipe author has already chosen the hyperparameters.

Configures the search_algorithm axis on L4_D_tuning (layer l4); the none value is materialised in the recipe’s fixed_axes block under that sub-layer.

When to use

Default. Studies with hand-picked hyperparameters.

References

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

Last reviewed 2026-05-04 by macroforecast author.

cv_path – operational

Regularisation path via RidgeCV / LassoCV.

Picks alpha from a grid via leave-one-out CV. Only applicable to ridge / lasso / elastic_net families.

When to use

Quick alpha selection; comparable to published cross-validated linear baselines.

References

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

Last reviewed 2026-05-04 by macroforecast author.

grid_search – operational

Exhaustive grid over leaf_config.tuning_grid.

Sklearn GridSearchCV with TimeSeriesSplit cross-validation. Requires leaf_config.tuning_grid.

When to use

Reproducible hyperparameter sweeps; comparison against published grid-tuned baselines.

References

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

Last reviewed 2026-05-04 by macroforecast author.

random_search – operational

Random sampling of tuning_distributions.

Sklearn RandomizedSearchCV. leaf_config.tuning_budget caps the iteration count.

When to use

Larger search spaces; black-box hyperparameter exploration.

References

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

Last reviewed 2026-05-04 by macroforecast author.

bayesian_optimization – operational

Optuna TPE optimisation (optional dependency).

Requires pip install macroforecast[tuning] (optuna). Falls back to random_search when optuna isn’t installed.

When to use

Expensive estimators where each fit costs many seconds; hyperparameter spaces with smooth landscapes.

References

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

Last reviewed 2026-05-04 by macroforecast author.

genetic_algorithm – operational

Tournament-selection genetic algorithm.

Crossover-style evolution over hyperparameter dictionaries. leaf_config.genetic_algorithm_population and ..._generations control budget.

When to use

Discrete / categorical hyperparameter spaces where TPE struggles.

References

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

Last reviewed 2026-05-04 by macroforecast author.