Investigating Different Scores from Identical Hyperparameter Settings

Summary

This forum post describes a reproducibility question in a quantitative modeling workflow. The author sets a learning-rate parameter to the same value in the model and in a hyperparameter-search module, and configures the search grid to contain only that value. The scoring function reads a mean squared error output from another module. Despite the nominally identical setting, running the model module by itself produces an MSE of 0.2386537, while running the full workflow through hyperparameter search reports a best score of 0.2388823926448822.

The post asks why these results differ but supplies no answer, diagnosis, or verified cause. It therefore serves as an example of a score mismatch to investigate, not as guidance on resolving one. A reader would need to inspect the workflow’s execution behavior and data handling to determine whether the runs differ in inputs, state, evaluation procedure, or other conditions; the post itself does not establish any of these explanations. It offers no broader modeling method, experiment, or evidence beyond the reported comparison.

Key ideas

• The post compares standalone module execution with a full run that includes hyperparameter search.
• The search grid contains only one learning-rate value, which is also set directly in the model module.
• The reported MSE values differ between the two execution paths.
• The author asks for an explanation but the document provides no resolution or confirmed cause.
• The comparison does not establish which workflow result is correct.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.