Book Image

Data Science Projects with Python - Second Edition

By : Stephen Klosterman
Book Image

Data Science Projects with Python - Second Edition

By: Stephen Klosterman

Overview of this book

If data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable. In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects. You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest. Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.
Table of Contents (9 chapters)
Preface

6. Gradient Boosting, XGBoost, and SHAP Values

Overview

After reading this chapter, you will be able to describe the concept of gradient boosting, the fundamental idea underlying the XGBoost package. You will then train XGBoost models on synthetic data, while learning about early stopping as well as several XGBoost hyperparameters along the way. In addition to using a similar method to grow trees as we have previously (by setting max_depth), you'll also discover a new way of growing trees that is offered by XGBoost: loss-guided tree growing. After learning about XGBoost, you'll then be introduced to a new and powerful way of explaining model predictions, called SHAP (SHapley Additive exPlanations). You will see how SHAP values can be used to provide individualized explanations for model predictions from any dataset, not just the training data, and also understand the additive property of SHAP values.