Question 3b: Precision and Recall While you may achieve high test accuracy, what about precision and recall? Precision (also called positive predictive value) is the fraction of true positives among the total number of data points predicted as positive. Recall (also known as sensitivity) is the fraction of true positives among the total number of data points with positive labels. Precision reflects the ability of your classifier to avoid predicting negative samples as positive (i.e., minimizing false positives), while recall reflects the classifier's ability to identify all positive samples (i.e., minimizing false negatives) Below is a graphical illustration of precision and recall, modified slightly from Wikipedia: relevant elements false negatives true negatives O How mary retrieved How many relevant items are relevant? items are retrieved T true positives |false positives Precision Recall retrieved elements Mathematically, precision and recall are defined as: Precision = Haruo positives Marue positives + Afalse positives Recall = - Atrue positives Mtruc positives + /false negatives Use the formulas above to compute the precision and recall for the test set using the Ir model trained with sklearn. [29]: MY_test_pred = ... precision = . . . recall = . .. print (f 'precision = {precision: .4f)") print (f'recall = {recall: .4f}")