predicting one class with acceptable performance does not always mean that the classifier will predict the other classes acceptably this can be seen in the following two graphs where we plot the precision/recall curves for classifying bad (left graph of the next screenshotand good (right graph of the next screenshotanswersin the previous graphswe have also included much better description of classifier' performancethe area under curve (aucthis can be understood as the average precision of the classifier and is great way of comparing different classifiers we see that we can basically forget about predicting bad answers (the left graph of the previous screenshotthis is because the precision for predicting bad answers decreases very quicklyat already very low recall valuesand stays at an unacceptably low percent predicting good answershowevershows that we can get above percent precision at recall of almost percent let us find out what threshold we need for that with the following codethresholds np hstack(([ ],thresholds[medium])idx precisions>= print(" = = thresh= (precision[idx ][ ]recall[idx ][ ]threshold[idx ][ ]) = = thresh=