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

Summary

In this chapter, we've implemented a XGBoost classification model. We've remembered the business scenario that was already used in Chapter 6, Classifying Trees with Multiclass Logistic Regression, based on the need to automatically classify New York City trees. After that, we've learned the basics of the XGBoost boosted tree classification model.

In order to build an effective model, we performed data quality checks and then segmented the dataset according to our needs into three tables: one to host training data, a second one for the evaluation stage, and a last one to apply our classification model.

During the training phase of the BigQuery ML model, we've constantly improved the performance of the ML model, using ROC AUC as a key performance indicator (KPI).

After that, we've evaluated the best ML model on a new set of records to avoid any overfitting, becoming more confident about the good quality of our XGBoost classification model.

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