Book Image

Automated Machine Learning with Microsoft Azure

By : Dennis Michael Sawyers
Book Image

Automated Machine Learning with Microsoft Azure

By: Dennis Michael Sawyers

Overview of this book

Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect.
Table of Contents (17 chapters)
Section 1: AutoML Explained – Why, What, and How
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

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...