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
Other Books You May Enjoy

Metrics for multi-label classification and recommendation problems

Recommender systems are one of the most popular applications of data analysis and machine learning, and there are quite a few competitions on Kaggle that have used the recommendation approach. For instance, the Quick, Draw! Doodle Recognition Challenge was a prediction evaluated as a recommender system. Some other competitions on Kaggle, however, truly strived to build effective recommender systems (such as Expedia Hotel Recommendations: and RecSYS, the conference on recommender systems (, even hosted one of its yearly contests on Kaggle (RecSYS 2013:

Mean Average Precision at K (MAP@{K}) is typically the metric of choice for evaluating the performance of recommender systems, and it is the most common metric you will encounter on Kaggle in all the competitions that try to build or approach...