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Power BI Machine Learning and OpenAI

You're reading from   Power BI Machine Learning and OpenAI Explore data through business intelligence, predictive analytics, and text generation

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781837636150
Length 308 pages
Edition 1st Edition
Languages
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Author (1):
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Greg Beaumont Greg Beaumont
Author Profile Icon Greg Beaumont
Greg Beaumont
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Data Exploration and Preparation
2. Chapter 1: Requirements, Data Modeling, and Planning FREE CHAPTER 3. Chapter 2: Preparing and Ingesting Data with Power Query 4. Chapter 3: Exploring Data Using Power BI and Creating a Semantic Model 5. Chapter 4: Model Data for Machine Learning in Power BI 6. Part 2: Artificial Intelligence and Machine Learning Visuals and Publishing to the Power BI Service
7. Chapter 5: Discovering Features Using Analytics and AI Visuals 8. Chapter 6: Discovering New Features Using R and Python Visuals 9. Chapter 7: Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service 10. Part 3: Machine Learning in Power BI
11. Chapter 8: Building Machine Learning Models with Power BI 12. Chapter 9: Evaluating Trained and Tested ML Models 13. Chapter 10: Iterating Power BI ML models 14. Chapter 11: Applying Power BI ML Models 15. Part 4: Integrating OpenAI with Power BI
16. Chapter 12: Use Cases for OpenAI 17. Chapter 13: Using OpenAI and Azure OpenAI in Power BI Dataflows 18. Chapter 14: Project Review and Looking Forward 19. Index 20. Other Books You May Enjoy

Iterating Power BI ML models

In Chapter 8, you trained Power BI ML models using all of the features that you had selected for each of the three ML models – that is, Predict Damage ML, Predict Size ML, and Predict Height ML – using data from the FAA Wildlife Strike database. In Chapter 9, you evaluated the test results of the automated training and testing process that is part of Power BI. The test results helped you understand the strengths and weaknesses of the predictive models, along with details about features that contributed to correct predictions.

This chapter will revisit the findings from Chapter 9 and use them to decide if you need to modify and retrain the ML models to achieve better results via iterative development. The list of features that are used to train these ML models can be whittled down, the filter criteria can be adjusted, and the result of the new round of training and testing can be compared to those from Chapter 9.

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