if instead our goal would have been to detect as much good or bad answers as possiblewe would be more interested in recallthe next screenshot shows all the good answers and the answers that have been classified as being good onesin terms of the previous diagramprecision is the fraction of the intersection of the right circle while recall is the fraction of the intersection of the left circle sohow can we optimize for precisionup to nowwe have always used as the threshold to decide whether an answer is good or not what we can do now is to count the number of tpfpand fn instances while varying that threshold between and with these countswe can then plot precision over recall the handy function precision_recall_curve(from the metrics module does all the calculations for us as shown in the following codefrom sklearn metrics import precision_recall_curve precisionrecallthresholds precision_recall_curve(y_testclf predict(x_test