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)

Gradient Boosting and XGBoost

What Is Boosting?

Boosting is a procedure for creating ensembles of many machine learning models, or estimators, similar to the bagging concept that underlies the random forest model. Like bagging, while boosting can be used with any kind of machine learning model, it is commonly used to build ensembles of decision trees. A key difference from bagging is that in boosting, each new estimator added to the ensemble depends on all the estimators added before it. Because the boosting procedure proceeds in sequential stages, and the predictions of ensemble members are added up to calculate the overall ensemble prediction, it is also called stagewise additive modeling. The difference between bagging and boosting can be visualized as in Figure 6.1:

Figure 6.1: Bagging versus boosting

While bagging trains many estimators using different random samples of the training data, boosting trains new estimators using information about which...