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)

RandomForest Variable Importance

Chapter 4, Multiclass Classification with RandomForest, introduced you to a very powerful tree-based algorithm: RandomForest. It is one of the most popular algorithms in the industry, not only because it achieves very good results in terms of prediction but also for the fact that it provides several tools for interpreting it, such as variable importance.

Remember from Chapter 4, Multiclass Classification with RandomForest, that RandomForest builds multiple independent trees and then averages their results to make a final prediction. We also learned that it creates nodes in each tree to find the best split that will clearly separate the observations into two groups. RandomForest uses different measures to find the best split. In sklearn, you can either use the Gini or Entropy measure for the classification task and MSE or MAE for regression. Without going into the details of each of them, these measures calculate the level of impurity of a given...