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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

Generating prediction explanations

In this section, we will focus on how to generate explanations along with predictions for the scoring dataset. After uploading the scoring dataset (as we discussed in the preceding section), you can now go to the Understand tab and then select the Prediction Explanations tab, as shown in the following screenshot:

Figure 8.4 – Prediction explanations

In the preceding screenshot, you can see that it now shows the scoring dataset that was uploaded. You can now click on the icon next to the dataset filename to compute the explanations. Once the computation is complete, you will see the download icon. You can use the download icon to download the generated explanations for the predictions made by the model. The explanations come in the form of a .csv file that can be opened using Excel, as shown in the following screenshot:

Figure 8.5 – Prediction explanations file

In the preceding screenshot...

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