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Agile Machine Learning with DataRobot

You're reading from   Agile Machine Learning with DataRobot Automate each step of the machine learning life cycle, from understanding problems to delivering value

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801076807
Length 344 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Bipin Chadha Bipin Chadha
Author Profile Icon Bipin Chadha
Bipin Chadha
Sylvester Juwe Sylvester Juwe
Author Profile Icon Sylvester Juwe
Sylvester Juwe
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Foundations
2. Chapter 1: What Is DataRobot and Why You Need It? FREE CHAPTER 3. Chapter 2: Machine Learning Basics 4. Chapter 3: Understanding and Defining Business Problems 5. Section 2: Full ML Life Cycle with DataRobot: Concept to Value
6. Chapter 4: Preparing Data for DataRobot 7. Chapter 5: Exploratory Data Analysis with DataRobot 8. Chapter 6: Model Building with DataRobot 9. Chapter 7: Model Understanding and Explainability 10. Chapter 8: Model Scoring and Deployment 11. Section 3: Advanced Topics
12. Chapter 9: Forecasting and Time Series Modeling 13. Chapter 10: Recommender Systems 14. Chapter 11: Working with Geospatial Data, NLP, and Image Processing 15. Chapter 12: DataRobot Python API 16. Chapter 13: Model Governance and MLOps 17. Chapter 14: Conclusion 18. Other Books You May Enjoy

Reviewing and understanding model details

In the last chapter, we created several models for different projects. DataRobot creates 10 to 20 models in a project, and it would be very onerous to look at and analyze the details of all of these models. You do not have to review each of these models, and it is common to review only the top few models before making a final selection. We will now look at the leaderboard for models in the Automobile Example 2 project and select the top model, as illustrated in the following screenshot:

Figure 7.1 – Model information

In the preceding screenshot, we selected the Model Info tab within the Describe tab to get a view of how large the model is and the expected time it takes to create predictions. This information is useful in real-time applications that are time-sensitive and need to score thousands of transactions quickly. Let's now go to the Feature Impact tab within the Understand tab, as shown in the following...

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