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Machine Learning with BigQuery ML

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

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Product type Paperback
Published in Jun 2021
Publisher Packt
ISBN-13 9781800560307
Length 344 pages
Edition 1st Edition
Languages
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Author (1):
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Alessandro Marrandino Alessandro Marrandino
Author Profile Icon Alessandro Marrandino
Alessandro Marrandino
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Table of Contents (20) Chapters Close

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

Training the binary logistic regression model

As we already did in Chapter 4, Predicting Numerical Values with Linear Regression, we'll adopt an incremental approach in trying to improve the performance of our ML model at each attempt:

  1. Let's start training our first ML model, binary_classification_version_1:
    CREATE OR REPLACE MODEL `05_chicago_taxi.binary_classification_version_1`
    OPTIONS
      (model_type='logistic_reg', labels = ['will_get_tip']) AS
        SELECT
            trip_seconds,
            IF(tips>0,1,0) AS will_get_tip
        FROM  `05_chicago_taxi.training_table`;

    In this BigQuery ML statement, we can see the CREATE OR REPLACE MODEL keywords used to start the training of the model. These keywords are followed by the identifier of the ML model. After the identifier, we can notice the OPTIONS clause. As our options...

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