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 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...