Configure data analysis binning, predictor selection, and other advanced settings that control how a Bayesian adaptive model operates.
- In the Advanced Settings section, choose the update scope:
- To use all received responses for each update cycle, click Use all responses.
- To assign more weight to recent responses when updating a model, click Use subset of responses.
- In the Monitor performance for the last field, enter the
number of weighted responses used to calculate the model performance that is
used in monitoring. The default setting is 0, which means that all historical data is to be used in performance monitoring.
- In the Data analysis binning section, in the
Grouping granularity field, enter a value between 0
and 1 that determines the granularity of the predictor binning. The higher the value, the more bins are created. The value represents a statistical threshold that indicates when predictor bins with similar behavior are merged. The default setting is 0.25.
- In the Grouping minimum cases field, enter 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 can be used to increase the robustness of the model. Lower values result in increasing the number of groups, which can be used to increase the performance of the model. The default setting is 0.05.
- In the Predictor selection section, in the
Activate predictors with a performance above field,
enter a value between 0 and 1 that determines the threshold for excluding poorly
performing predictors. The value is measured as the Area Under the Curve (AUC) of the predictor as compared to the outcome. A higher value results in fewer predictors in the final model. The minimum performance of AUC is 0.5, therefore the value of the performance threshold should always be set to at least 0.5. The default setting is 0.52.
- In the Group predictors with a correlation above field,
enter a value between 0 and 1 that determines the threshold for excluding
correlated predictors. The default setting is 0.8. Predictors that have a mutual correlation above this threshold are considered similar, and only the best of those predictors are used for adaptive learning. The measure is the correlation between the probabilities of positive behavior of pairs of predictors.