The Credit Scoring zone is where the [applications_validation](dataset:applications_validation) dataset gets scored using the previously built model. Then, some statistics about the quality of the model can be inferred.

![Credit Scoring.png](X0tjsqojolUn)

The [python recipe](recipe:compute_applications_scored) takes the scorecard to score the validation set. The computation is done by calling a function written in the project library. It matches each feature with their bin to get their score, the final score is then the sum of the scores for each feature.

The next two steps build the aggregations to output some statistics about the model. In the [prepare recipe](recipe:compute_scores), the total score for each applicant is computed by adding up the scores from each feature. Then a binned score is built to regroup scores. The final [grouping recipe](recipe:compute_scores_binned) computes the average score and credit_event frequency per binned score to obtain the graph that is exposed in the [dashboard](article:29).


