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)
Preface
1
Part I: Introduction to Competitions
6
Part II: Sharpening Your Skills for Competitions
15
Part III: Leveraging Competitions for Your Career
18
Other Books You May Enjoy
19
Index

Ensembling with Blending and Stacking Solutions

When you start competing on Kaggle, it doesn’t take long to realize that you cannot win with a single, well-devised model; you need to ensemble multiple models. Next, you will immediately wonder how to set up a working ensemble. There are few guides around, and more is left to Kaggle’s lore than to scientific papers.

The point here is that if ensembling is the key to winning in Kaggle competitions, in the real world it is associated with complexity, poor maintainability, difficult reproducibility, and hidden technical costs for little advantage. Often, the small boost that can move you from the lower ranks to the top of the leaderboard really doesn’t matter for real-world applications because the costs overshadow the advantages. However, that doesn’t mean that ensembling is not being used at all in the real world. In a limited form, such as averaging and mixing a few diverse models, ensembling allows us...