You're reading from Machine Learning with BigQuery ML Create, execute, and improve machine learning models in BigQuery using standard SQL queries

Product type Paperback

Published in Jun 2021

Publisher Packt

ISBN-13 9781800560307

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

BigQuery

Concepts

Machine Learning

Author (1):

Alessandro Marrandino

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Table of Contents (20) Chapters

Preface

1. Section 1: Introduction and Environment Setup

2. Chapter 1: Introduction to Google Cloud and BigQuery FREE CHAPTER

3. Chapter 2: Setting Up Your GCP and BigQuery Environment

4. Chapter 3: Introducing BigQuery Syntax

5. Section 2: Deep Learning Networks

6. Chapter 4: Predicting Numerical Values with Linear Regression

7. Chapter 5: Predicting Boolean Values Using Binary Logistic Regression

8. Chapter 6: Classifying Trees with Multiclass Logistic Regression

9. Section 3: Advanced Models with BigQuery ML

10. Chapter 7: Clustering Using the K-Means Algorithm

11. Chapter 8: Forecasting Using Time Series

12. Chapter 9: Suggesting the Right Product by Using Matrix Factorization

13. Chapter 10: Predicting Boolean Values Using XGBoost

14. Chapter 11: Implementing Deep Neural Networks

15. Section 4: Further Extending Your ML Capabilities with GCP

16. Chapter 12: Using BigQuery ML with AI Notebooks

17. Chapter 13: Running TensorFlow Models with BigQuery ML

18. Chapter 14: BigQuery ML Tips and Best Practices

19. Other Books You May Enjoy

Chapter 10: Predicting Boolean Values Using XGBoost

eXtreme Gradient Boosting (XGBoost) is one of the most powerful machine learning (ML) libraries that data scientists can leverage to solve complex use cases in an efficient and flexible way. It started as a research project, and the first version was released in 2014. The popularity of this ML library grew very quickly, thanks to its capabilities and portability. In fact, it was used in important Kaggle ML contests and is now available for different programming languages and on different operating systems.

This library can be used to tackle different ML problems and is specifically designed for structured data. XGBoost was also recently released for BigQuery ML. Thanks to this technique, BigQuery users are allowed to implement classification and regression ML models using this library.

In this chapter, we'll see all the stages necessary to implement a XGBoost classification model to classify New York City trees into different...