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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In competitions where the number of examples used for training can make a difference, pseudo-labeling can boost your scores by providing further examples taken from the test set. The idea is to add examples from the test set whose predictions you are confident about to your training set.

First introduced in the Santander Customer Transaction Prediction competition by team Wizardry (read here:, pseudo-labeling simply helps models to refine their coefficients thanks to more data available, but it won’t always work. First of all, it is not necessary in some competitions. That is, adding pseudo-labels won’t change the result; it may even worsen it if there is some added noise in the pseudo-labeled data.

Unfortunately, you cannot know for sure beforehand whether or not pseudo-labeling will work in a competition (you have to test it empirically), though plotting...