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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

Snooping on the leaderboard

As we previously described, in each competition, Kaggle divides the test set into a public part, which is visualized on the ongoing leaderboard, and a private part, which will be used to calculate the final scores. These test parts are usually randomly determined (although in time series competitions, they are determined based on time) and the entire test set is released without any distinction made between public and private.

Recently, in order to avoid the scrutinizing of test data in certain competitions, Kaggle has even held back the test data, providing only some examples of it and replacing them with the real test set when the submission is made. These are called Code competitions because you are not actually providing the predictions themselves, but a Notebook containing the code to generate them.

Therefore, a submission derived from a model will cover the entire test set, but only the public part will immediately be scored, leaving...

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