thus based on this informationcan you compute the previously mentioned metrics for our confusion matrix based on the model predictions on the breast cancer test datapositive_class tp fp tn fn performance metrics the confusion matrix by itself is not performance measure for classification models but it can be used to calculate several metrics that are useful measures for different scenarios we will describe how the major metrics can be calculated from the confusion matrixcompute them manually using necessary formulaeand then compare the results with functions provided by scikit-learn on our predicted results and give an intuition of scenarios where each of those metric can be used accuracythis is one of the most popular measures of classifier performance it is defined as the overall accuracy or proportion of correct predictions of the model the formula for computing accuracy from the confusion matrix isaccuracy tp tn tp fp tn fn accuracy measure is normally used when our classes are almost balanced and correct predictions of those classes are equally important the following code computes accuracy on our model predictions fw_acc round(meu metrics accuracy_score(y_true=y_testy_pred=y_pred) mc_acc round((tp tn(tp tn fp fn) print('framework accuracy:'fw_accprint('manually computed accuracy:'mc_accframework accuracy manually computed accuracy precisionprecisionalso known as positive predictive valueis another metric that can be derived from the confusion matrix it is defined as the number of predictions made that are actually correct or relevant out of all the predictions based on the positive class the formula for precision is as followsprecision tp tp fp model with high precision will identify higher fraction of positive class as compared to model with lower precision precision becomes important in cases where we are more concerned about finding the maximum number of positive class even if the total accuracy reduces the following code computes precision on our model predictions fw_prec round(meu metrics precision_score(y_true=y_testy_pred=y_pred) mc_prec round((tp(tp fp) print('framework precision:'fw_precprint('manually computed precision:'mc_prec