About
Explainability refers to having an understanding of why a model makes a certain prediction. This typically comes in form of knowing the relationship between a model's prediction and the input features used to generate said prediction (text, pixels, features, etc.). Linear models like linear regression, and ensemble models like decision trees are known to be easily interpretable. Deep learning models are black boxes, which makes it much harder to understand how those models make predictions. In this Guided Project, we will use SHAP, a common model-agnostic explainability method, to calculate the contributions of each feature to the prediction for various types of models.

A Look at the Project Ahead
After completing this guided project you will be able to:

What You'll Need
This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.

 Frequently Asked Questions

> Do I need to install any software to participate in this project?

Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.

> What web browser should I use?

The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.

Your Instructor
Kopal Garg
I am a Data Scientist Intern at IBM, and a Masters student in computer science at the University of Toronto. I am passionate about building AI-based solutions that improve various aspects of human life.