now that we have our predictionslet' evaluate how well our classifier is performing step -evaluating the model' accuracy using the array of true class labelswe can evaluate the accuracy of our model' predicted values by comparing the two arrays (test_labels vs predswe will use the sklearn function accuracy_score(to determine the accuracy of our machine learning classifier ml tutorial from sklearn metrics import accuracy_score evaluate accuracy print(accuracy_score(test_labelspreds)you'll see the following resultsalt jupyter notebook with python cell that prints the accuracy of our nb classifier as you see in the outputthe nb classifier is accurate this means that percent of the time the classifier is able to make the correct prediction as to whether or not the tumor is malignant or benign