Specify the missing value strategy and stopping rules for the Optimizer

Fit a TreeNet^®, Random Forests^®, or MARS^®model from the Predictive Analytics Module. Select Response Optimizer in the results. Select Optimizer.

Missing Value Strategy

Usually, Dynamic works well. The dynamic strategy precisely models the probability of missing for a given predictor variable and estimates the probability of a missing value given the values of other predictors. For example, with the dynamic strategy, the analysis can assign a higher probability of a missing value to X1 when X2 is greater than 10. The heuristic strategy does not model the probability of a missing value. Consider the heuristic strategy when the data have missing values and the analysis takes too long to find a satistfactory solution.

Stopping Rules

Specify when to stop the search for an optimal solution. Ideally, the search finds a solution with a desirability of 1 and the values of the predictors are satisfactory. Usually, you lengthen the search to try to find a solution with a higher desirability.

Time in minutes exceeds: Increase the time to try more solutions. Enter a value of 0 or greater.; Small values let you get a solution quickly, such as if you want to show sample output but do not need a solution with high desirability. For example, a value of 0 provides a solution from the first iteration.
Iterations exceed: Usually, you set a time instead of a number of iterations because the time to complete a number of iterations varies from data set to data set. Specify a greater number of iterations to try more solutions.; Small values let you get a solution quickly, such as if you want to show sample output but do not need a solution with high desirability. For example, a value of 0 provides a solution from the first iteration.

Composite desirability is greater than or equal to: Deselect this option to extend the search until the other stopping rule that you specify applies.; Decrease the value from 1 to try to shorten the search. The search completes at the first iteration where at least 1 solution has the lower desirability.