ILLM measure for covering quality
Generally it is accepted that good models satisfy many positive examples and no, or very small number of, negative examples. It is easy to apply this principle when we need to compare two models which satisfy no negative example and one satisfies more positive examples than the other one. But if the first model which satisfies more positive examples, satisfies also one or two negative examples, then the decision might be not so easy.
This problem is in ILLM solved so that a measure of the covering quality is defined as:
- TP
- total number of positive training examples correctly classified by the model
- FP
- total number of negative training examples incorrectly classified by the model as the positive cases
- g
- generalization level selected by the user.
All potentially good models must satisfy the condition of high quality generalization of the submitted examples. The covering quality measure serves for selection among potentially good models. In connection with this formula, few important things should be noted:
© 2001 LIS - Rudjer Boskovic Institute
Last modified: January 23 2018 13:51:13.