false negatives (fn)it is the case when actual class of data point is predicted class of data point is we can use confusion_matrix function of sklearn metrics to compute confusion matrix of our classification model classification accuracy it is most common performance metric for classification algorithms it may be defined as the number of correct predictions made as ratio of all predictions made we can easily calculate it by confusion matrix with the help of following formulaaccuracy tp tn tp fp fn tn we can use accuracy_score function of sklearn metrics to compute accuracy of our classification model classification report this report consists of the scores of precisionsrecallf and support they are explained as followsprecision precisionused in document retrievalsmay be defined as the number of correct documents returned by our ml model we can easily calculate it by confusion matrix with the help of following formulaprecision tp tp fp recall or sensitivity recall may be defined as the number of positives returned by our ml model we can easily calculate it by confusion matrix with the help of following formularecall tp tp fn specificity specificityin contrast to recallmay be defined as the number of negatives returned by our ml model we can easily calculate it by confusion matrix with the help of following formulaspecificity tn tn fp