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The Kaggle Book

You're reading from   The Kaggle Book Data analysis and machine learning for competitive data science

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801817479
Length 534 pages
Edition 1st Edition
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Konrad Banachewicz Konrad Banachewicz
Author Profile Icon Konrad Banachewicz
Konrad Banachewicz
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Toc

Table of Contents (20) Chapters Close

Preface
1. Part I: Introduction to Competitions
2. Introducing Kaggle and Other Data Science Competitions FREE CHAPTER 3. Organizing Data with Datasets 4. Working and Learning with Kaggle Notebooks 5. Leveraging Discussion Forums 6. Part II: Sharpening Your Skills for Competitions
7. Competition Tasks and Metrics 8. Designing Good Validation 9. Modeling for Tabular Competitions 10. Hyperparameter Optimization 11. Ensembling with Blending and Stacking Solutions 12. Modeling for Computer Vision 13. Modeling for NLP 14. Simulation and Optimization Competitions 15. Part III: Leveraging Competitions for Your Career
16. Creating Your Portfolio of Projects and Ideas 17. Finding New Professional Opportunities 18. Other Books You May Enjoy
19. Index

Tuning your model validation system

At this point, you should have a complete overview of all possible validation strategies. When you approach a competition, you devise your validation strategy and you implement it. Then, you test if the strategy you have chosen is correct.

As a golden rule, be guided in devising your validation strategy by the idea that you have to replicate the same approach used by the organizers of the competition to split the data into training, private, and public test sets. Ask yourself how the organizers have arranged those splits. Did they draw a random sample? Did they try to preserve some specific distribution in the data? Are the test sets actually drawn from the same distribution as the training data?

These are not the questions you would ask yourself in a real-world project. Contrary to a real-world project where you have to generalize at all costs, a competition has a much narrower focus on having a model that performs on the given test set...

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