In previous modules, we explored binary classification, where there were only two possible categories, or classes. When we have three or more categories, we call the problem a multiclass classification problem. There are a few different methods of doing multiclass classification and in this module, we'll focus on the one-versus-all method. The one-versus-all method is a technique where we choose a single category as the Positive case and group the rest of the categories as the False case. We're essentially splitting the problem into multiple binary classifications.

The dataset we will work with contains information on various cars. We have information about the technical aspects of each vehicle, such as the motor's displacement, the weight of the car, the miles per gallon, and how fast the car accelerates. Using this information, we will predict the origin of the vehicle, either North America, Europe, or Asia.

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 to extend logistic regression to work with multiple categories.
  • Learn to create dummy variables.

Lesson Outline

1. Introduction to the data
2. Dummy variables
3. Multiclass classification
4. Training a multiclass logistic regression model
5. Testing the models
6. Choose the origin
7. Next Steps
8. Takeaways