Final answer:
Increasing the performance threshold setting of an adaptive model rule often results in the decrease in the number of active predictors as the model becomes more selective in considering only those predictors with stronger statistical significance.
Step-by-step explanation:
When you increase the performance threshold setting of an adaptive model rule, it implies that the model will require a higher level of confidence in a predictor's effect on the predicted outcome before it is considered 'active'. Therefore, increasing the performance threshold setting can actually lead to the decrease in the number of active predictors because only those predictors with a stronger statistical significance are allowed to contribute to the model. This process does not inherently increase the performance of the model; instead, it refines the model to be more selective about the predictors it includes, with the aim of improving overall model accuracy by focusing on the most informative factors.