A bank has searched learning rate, penalty strength, and class weight inside one logistic-regression family for loan-default risk. The features are already limited to what the credit policy can supply, and the held-out metric has stopped moving. What is the sensible next design step?

Tuning learning rate, penalty strength, and class weight explores settings within the logistic-regression family. Those settings have been searched, the credit policy already limits which features you can use, and the held-out metric has stopped improving. Together, this suggests that the logistic model’s form—not simply an undiscovered penalty value—may be limiting performance. The sensible next design step is to try a different model family, which may represent structure that a linear log-odds model cannot. Searching a finer grid remains within logistic regression and spends more effort on settings after the held-out results have stalled. Changing only random seeds does not give the model a different form, so it cannot address that limitation. Choosing the training-set winner discards the held-out evidence and still keeps the search within logistic regression. Exam caveat: a different family is justified here because the policy has already fixed the available features; if richer features were permitted, changing the feature set would be an earlier design step. Practical check: record the searched ranges and flat held-out curve, then name the next model family in the design log before launching another logistic grid.