Return observed best params in tuning #196
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Summary: Return observed best parameters instead of model-predicted best by setting use_model_predictions=False in get_best_parameterization(). With few tuning trials, the Bayesian surrogate model may not be well-calibrated and could return suboptimal parameters that don't match the actual best observed trial. This has confused users in the past and looks like a bug in the tutorial notebook. Differential Revision: D91884588
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Summary: Pull Request resolved: facebookincubator#196 Return observed best parameters instead of model-predicted best by setting use_model_predictions=False in get_best_parameterization(). With few tuning trials, the Bayesian surrogate model may not be well-calibrated and could return suboptimal parameters that don't match the actual best observed trial. This has confused users in the past and looks like a bug in the tutorial notebook. Differential Revision: D91884588
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Summary: Add a `use_model_predictions` parameter to `tune_mcgrad_params` with `False` as the default. When False, returns the actual observed best trial parameters. When True, uses the Bayesian surrogate model's predicted best parameters. The default is False because with few tuning trials, the surrogate model has high uncertainty and may predict a different optimum than the best observed trial. Users who want the model-predicted best can set this to True. Reviewed By: Lorenzo-Perini Differential Revision: D91884588
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
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Summary: Add a `use_model_predictions` parameter to `tune_mcgrad_params` with `False` as the default. When False, returns the actual observed best trial parameters. When True, uses the Bayesian surrogate model's predicted best parameters. The default is False because with few tuning trials, the surrogate model has high uncertainty and may predict a different optimum than the best observed trial. Users who want the model-predicted best can set this to True. Reviewed By: Lorenzo-Perini Differential Revision: D91884588
flinder
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Summary: Add a `use_model_predictions` parameter to `tune_mcgrad_params` with `False` as the default. When False, returns the actual observed best trial parameters. When True, uses the Bayesian surrogate model's predicted best parameters. The default is False because with few tuning trials, the surrogate model has high uncertainty and may predict a different optimum than the best observed trial. Users who want the model-predicted best can set this to True. Reviewed By: Lorenzo-Perini Differential Revision: D91884588
flinder
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Jan 30, 2026
Summary: Add a `use_model_predictions` parameter to `tune_mcgrad_params` with `False` as the default. When False, returns the actual observed best trial parameters. When True, uses the Bayesian surrogate model's predicted best parameters. The default is False because with few tuning trials, the surrogate model has high uncertainty and may predict a different optimum than the best observed trial. Users who want the model-predicted best can set this to True. Reviewed By: Lorenzo-Perini Differential Revision: D91884588
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This pull request has been merged in ca9fb7b. |
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Summary:
Return observed best parameters instead of model-predicted best by setting
use_model_predictions=False in get_best_parameterization(). With few tuning
trials, the Bayesian surrogate model may not be well-calibrated and could
return suboptimal parameters that don't match the actual best observed trial.
This has confused users in the past and looks like a bug in the tutorial notebook.
Differential Revision: D91884588