The performance of a model is an indication of its predictive power. If the performance is low, the scores of the model have a low statistical correlation with the outcome that is to be predicted. The minimum value for the Coefficient of Concordance is 50.

There are a number of reasons why adaptive models might have low performance:

• Adaptive models miss some values for predictors.
• The values for predictors are not set.
• The values for parameterized predictors are not set.
• No active predictors are in the adaptive models.
• The adaptive models do not receive any responses or sufficient responses.
• The potential predictors available to the models are not correlated to the outcome.

1. Depending on the cause, a system architect can perform the following actions:
   • Consider adding or removing predictors based on their performance.
   • Change the settings in the adaptive model rule instance that is used to generate adaptive models.
   • Try the solutions for the following issues:
     • Values of the predictors are not set
     • Values of the parameterized predictors are not set
     • Adaptive models do not contain any active predictors, or contain only one active predictor