Final answer:
When you increase the performance threshold setting of an adaptive model rule, the performance of the model is increased. However, the correlation threshold decreases and the number of active predictors may decrease or increase.
Step-by-step explanation:
When you increase the performance threshold setting of an adaptive model rule, the performance of the model is increased. This means that the model's accuracy and effectiveness in predicting outcomes or making decisions improves.
However, increasing the performance threshold can also result in some changes. One of the changes is that the correlation threshold decreases. The correlation threshold is a measure of how closely related the predictors are to the outcome. When the performance threshold is increased, the correlation threshold is lowered, meaning less emphasis is placed on the relationship between predictors and the outcome.
Additionally, increasing the performance threshold can lead to a change in the number of active predictors. Active predictors are the variables or factors that the model uses to make predictions. In some cases, the number of active predictors may decrease because the model becomes more selective and only considers the predictors that have a high impact on the outcome. However, in other cases, the number of active predictors increases as the model incorporates more factors to improve its performance.