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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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Toc

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

Data visualization

One of the most important tasks a data analyst or data scientist needs to do is to understand the dataset. Data visualization is key to this understanding. DataRobot provides various ways to visualize the datasets to help you understand the dataset. These visualizations are built automatically for you so that you can spend your time analyzing them instead of preparing them. Let's look at what these are and how to use them.

When you go to the data page (Figure 1.20) for your project, you will see high-level profile information for your dataset. Inspect this information carefully to understand your dataset in totality. If you click on the Feature Association menu (top left), you will see how the features are related to one another (Figure 2.3):

Figure 2.3 – Feature associations using mutual information

This diagram shows the interrelationships using the mutual information metric. Mutual Information (MI) uses information theory...

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