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Azure Data Scientist Associate Certification Guide

You're reading from  Azure Data Scientist Associate Certification Guide

Product type Book
Published in Dec 2021
Publisher Packt
ISBN-13 9781800565005
Pages 448 pages
Edition 1st Edition
Languages
Authors (2):
Andreas Botsikas Andreas Botsikas
Profile icon Andreas Botsikas
Michael Hlobil Michael Hlobil
Profile icon Michael Hlobil
View More author details

Table of Contents (17) Chapters

Preface Section 1: Starting your cloud-based data science journey
Chapter 1: An Overview of Modern Data Science Chapter 2: Deploying Azure Machine Learning Workspace Resources Chapter 3: Azure Machine Learning Studio Components Chapter 4: Configuring the Workspace Section 2: No code data science experimentation
Chapter 5: Letting the Machines Do the Model Training Chapter 6: Visual Model Training and Publishing Section 3: Advanced data science tooling and capabilities
Chapter 7: The AzureML Python SDK Chapter 8: Experimenting with Python Code Chapter 9: Optimizing the ML Model Chapter 10: Understanding Model Results Chapter 11: Working with Pipelines Chapter 12: Operationalizing Models with Code Other Books You May Enjoy

Interpreting the predictions of the model

Being able to interpret the predictions of a model helps data scientists, auditors, and business leaders understand model behavior by looking at the top important factors that drive the model's predictions. It also enables them to perform what-if analysis to validate the impact of features on predictions. The Azure Machine Learning workspace integrates with InterpretML to provide these capabilities.

InterpretML is an open source community that provides tools to perform model interpretability. The community contains a couple of projects. The most famous ones are as follows:

  • Interpret and Interpret-Community repositories, which focus on interpreting models that use tabular data, such as the diabetes dataset you have been working on within this book. You are going to work with the interpret-community repository in this section.
  • interpret-text extends the interpretability efforts into text classification models.
  • Diverse...
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