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

View More author details

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

Discovering multiclass logistic regression

In this section, we'll learn the basics of multiclass logistic regression and when this technique can be applied.

Multiclass logistic regression is a classification technique that can be used to categorize events, objects, customers, or other entities into multiple classes. Different from binary logistic regression, this ML algorithm can be used to classify output values into more than two discrete classes.

In order to predict one of the multiple labels, this ML algorithm calculates the probability of each outcome and selects the label with the highest probability.

Being a regression algorithm, the prediction of the label is based on a set of independent variables called features that are used to predict the dependent variable, called a label.

This ML technique can be used to answer business questions, such as the following: