When you click on View Cases this is an example of what you will see. The cases are listed row by row.
You can also see:
- All the cases that belong to each of the four categories in the Confusion Matrix are labelled and color coded (see Status column). Use the Sort facility in Excel to sort the cases into the four categories.
- Exemplar (i.e. modal) cases for each of these four types are those cases that have the highest degree of similarity of attributes with all other case in the same group. There may be more than one. These will have the lowest Hamming Distance values These are shown by the highlighted rows to the right of column E
- Outlier cases for each of the four types. These are cases that have the lowest level of similarity of attributes with all other case in the same group. These will have the highest Hamming Distance values
The next step is to select cases for subsequent within-case investigations, to identify casual mechanisms that may be at work underlying the associations represented in the predictive model. These types of cases may be useful:
- Modal case within the True Positive group. This is where a causal mechanism needs to be found that might apply to all other cases in this group
- Outlier cases within the True Positive group. Ideally the same mechanism will also be found here
- Modal cases within the False Positive Cases. These are cases with the same (predictive model) attributes but different (absent) outcomes. Here the same causal mechanism might be expected but along with other features that prevent them from working and delivering the outcome
- Modal cases within the False Negative Cases. These are cases with different (predictive model) attributes but the same (present) outcomes. Here the same causal mechanism should not be expected to be found.
See within-case analysis for more information on the kinds of cases selections and analyses that can be made at this stage.