Book Image

The Data Science Workshop

By : Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare
Book Image

The Data Science Workshop

By: Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare

Overview of this book

You already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.
Table of Contents (18 chapters)

Local Interpretation with LIME

After training our model, we usually use it for predicting outcomes on unseen data. The global interpretations we saw earlier, such as model coefficient, variable importance, and the partial dependence plot, gave us a lot of information on the features at an overall level. Sometimes we want to understand what has influenced the model for a specific case to predict a specific outcome. For instance, if your model is to assess the risk of offering credit to a new client, you may want to understand why it rejected the case for a specific lead. This is what local interpretation is for: analyzing a single observation and understanding the rationale behind the model's decision. In this section, we will introduce you to a technique called Locally Interpretable Model-Agnostic Explanations (LIME).

If we are using a linear model, it is extremely easy to understand the contribution of each variable to the predicted outcome. We just need to look at the...