What's a best practice for Hyperopt workflow?

Machine Learning

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Choose what hyperparameters are reasonable to optimize
Define broad ranges for each of the hyperparameters (including the default where applicable)
Run a small number of trials
Observe the results in an MLflow parallel coordinate plot and select the runs with lowest loss
Move the range towards those higher/lower values when the best runs’ hyperparameter values are pushed against one end of a range
Determine whether certain hyperparameter values cause fitting to take a long time (and avoid those values)
Re-run with more trials
Repeat until the best runs are comfortably within the given search bounds and none are taking excessive time

https://databricks.com/blog/2021/04/15/how-not-to-tune-your-model-with-hyperopt.html