Data analysis, grouping, and performance settings for adaptive models.
Field | Description |
| Run data analysis after | The value that determines the number of interaction results that trigger running data analysis for a model. Data analysis is triggered after the number of interaction results configured in this setting is reached. This setting should be configured according to the resources available to the ADM system and taking into account the minimum set of responses required for models to evolve. The default setting is 500. |
| Grouping granularity | A value between 0 and 1 that determines the granularity of predictor groups; higher values result in more groups, lower values in less groups. This setting establishes the threshold for a statistical measure that indicates the distinctive behavior between predictors groups. If the measure is above, the groups indicate significant distinctive behavior, otherwise they will be collapsed. The default setting is 0.25. |
| Grouping minimum cases | A value between 0 and 1 that determines the minimum percentage of cases per interval. Higher values result in decreasing the number of groups, which may be used to increase the robustness of the model. Lower values result in increasing the number of groups, which may be used to increase the performance of the model. The default setting is 0.05. Note: The Grouping granularity and minimum cases settings work in conjunction to control how predictor grouping is established. The fact that a predictor has more groups typically increases the performance, but the model may become less robust. |
| Performance threshold | A value between 0 and 1 that determines the threshold for excluding poorly performing predictors. The default setting is 0.52. |
| Correlation threshold | A value between 0 and 1 that determines the threshold for excluding correlated predictors. The default setting is 0.8. |