Book Image

The Kaggle Book

By : Konrad Banachewicz, Luca Massaron
5 (2)
Book Image

The Kaggle Book

5 (2)
By: Konrad Banachewicz, Luca Massaron

Overview of this book

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!
Table of Contents (20 chapters)
Part I: Introduction to Competitions
Part II: Sharpening Your Skills for Competitions
Part III: Leveraging Competitions for Your Career
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Using adversarial validation

As we have discussed, cross-validation allows you to test your model’s ability to generalize to unseen datasets coming from the same distribution as your training data. Hopefully, since in a Kaggle competition you are asked to create a model that can predict on the public and private datasets, you should expect that such test data is from the same distribution as the training data. In reality, this is not always the case.

Even if you do not overfit to the test data because you have based your decision not only on the leaderboard results but also considered your cross-validation, you may still be surprised by the results. This could happen in the event that the test set is even slightly different from the training set on which you have based your model. In fact, the target probability and its distribution, as well as how the predictive variables relate to it, inform your model during training about certain expectations that cannot be satisfied...