Personalizer's effectiveness in Apprentice mode will rarely achieve near 100% of the application's baseline; and never exceed it.
The best practice would be not aim for 100% attainment; but a range of 60% – 80% should be achievable depending on the use case.
However, if the learning performance is slow or plateaued below 60%, then the following issues may have occurred:
Not enough features sent with Rank API call
Bugs in the features sent - such as sending non-aggregated feature data such as timestamps to Rank API
Bugs with loop processing - such as not sending reward data to Reward API for events
To address these issues, you may need to make adjustments by either changing the features sent to the loop, or ensuring that the reward score is accurately capturing the value of the action returned by the Rank API call.