• 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