for the first classthe digit there are samples in the classand all of these samples were classified as class (there are no false negatives for class we can see that because all other entries in the first row of the confusion matrix are we can also see that no other digits were mistakenly classified as because all other entries in the first column of the confusion matrix are (there are no false positives for class some digits were confused with othersthough--for examplethe digit (third row)three of which were classified as the digit (fourth columnthere was also one digit that was classified as (third columnfourth rowand one digit that was classified as (thrid columnfourth rowwith the classification_report functionwe can compute the precisionrecalland -score for each classin[ ]print(classification_report(y_testpred)out[ ] precision recall -score support avg total model evaluation and improvement