Book Image

Machine Learning with BigQuery ML

By : Alessandro Marrandino
Book Image

Machine Learning with BigQuery ML

By: Alessandro Marrandino

Overview of this book

BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.
Table of Contents (20 chapters)
Section 1: Introduction and Environment Setup
Section 2: Deep Learning Networks
Section 3: Advanced Models with BigQuery ML
Section 4: Further Extending Your ML Capabilities with GCP

Chapter 6: Classifying Trees with Multiclass Logistic Regression

Multiclass logistic regression is the Machine Learning (ML) algorithm used to classify events, entities, and behaviors into a fixed number of categories. It can be used across different industries and business scenarios when it's necessary to predict the classification of an entity into multiple groups. A typical classification use case is represented by the desire to segment the customer base of a company according to their profitability and preferences in order to target the right customers with the most effective marketing campaigns.

This kind of technique is an extension of the binary logistic regression that allows us to overcome the limits of two possible labels and opens the applicability to other contexts where we can find multiple categories to identify.

In this chapter, we'll see all the stages necessary to implement, evaluate, and test a multiclass logistic regression model leveraging BigQuery...