In the previous lesson, we learned about classification, logistic regression, and how to use scikit-learn to fit a logistic regression model to a data set on graduate school adlessons. We'll continue to work with the dataset, which contains data on 644 applications and whether each applicant was admitted to the program.

In this lesson, you'll learn about some of the different ways to evaluate how well a binary classification model performs. Because we have binary or discrete values, the error metrics that we have learned for evaluation of a Linear Regression model's performance doesn't make much sense. 

To combat this and make it possible to evaluate a binary classification model, you will learn different measures such as accuracy, sensitivity, and specificity. You’ll also learn what each measure means and why they're important when evaluating the quality of a binary classification model.

As you work through each concept, you’ll get to apply what you’ve learned from within your browser — there's no need to use your own machine to do the exercises. The Python environment inside of this course includes answer checking so you can ensure that you've fully mastered each concept before learning the next.


  • Learn the different ways of evaluating accuracy of a classification model.
  • Learn to understand model performace using sensitivity and specificity.

Lesson Outline

1. Introduction to the Data
2. Accuracy
3. Binary classification outcomes
4. Binary classification outcomes
5. Sensitivity
6. Specificity
7. Next steps
8. Takeaways