You're reading from Automated Machine Learning with Microsoft Azure Build highly accurate and scalable end-to-end AI solutions with Azure AutoML

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800565319

Length 340 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Machine Learning

Authors (2):

Dennis Michael Sawyers

Dennis Sawyers

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: AutoML Explained – Why, What, and How

2. Chapter 1: Introducing AutoML FREE CHAPTER

3. Chapter 2: Getting Started with Azure Machine Learning Service

4. Chapter 3: Training Your First AutoML Model

5. Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide

6. Chapter 4: Building an AutoML Regression Solution

7. Chapter 5: Building an AutoML Classification Solution

8. Chapter 6: Building an AutoML Forecasting Solution

9. Chapter 7: Using the Many Models Solution Accelerator

10. Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

11. Chapter 8: Choosing Real-Time versus Batch Scoring

12. Chapter 9: Implementing a Batch Scoring Solution

13. Chapter 10: Creating End-to-End AutoML Solutions

14. Chapter 11: Implementing a Real-Time Scoring Solution

15. Chapter 12: Realizing Business Value with AutoML

16. Other Books You May Enjoy

Fine-tuning your AutoML classification model

In this section, you will first review tips and tricks for improving your AutoML classification models and then review the algorithms used by AutoML for both binary and multiclass classification.

Improving AutoML classification models

Keeping in mind the tips and tricks from Chapter 4, Building an AutoML Regression Solution, here are new ones that are specific to classification:

Unlike regression problems, nearly all classification problems in the real world require you to weigh your target column. The reason is that, for most business problems, one class is nearly always more important than the others.
For example, imagine you are running a business and you are trying to predict which customers will stop doing business with you and leave you for a competitor. This is a common problem called customer churn or customer turnover. If you misidentify a customer as being likely to churn, all you waste is an unnecessary phone call...