Grokking Machine Learning

7 How do you measure classification models? Accuracy and its friends

In this chapter

• types of errors a model can make: false positives and false negatives
• putting these errors in a table: the confusion matrix
• what are accuracy, recall, precision, F-score, sensitivity, and specificity, and how are they used to evaluate models
• what is the ROC curve, and how does it keep track of sensitivity and specificity at the same time

This chapter is slightly different from the previous two—it doesn’t focus on building classification models; instead, it focuses on evaluating them. For a machine learning professional, being able to evaluate the performance ...