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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Neural networks for tabular competitions

Having discussed neural networks with DAEs, we have to complete this chapter by discussing how neural networks can help you in a tabular competition more generally. Gradient boosting solutions still clearly dominate tabular competitions (as well as real-world projects); however, sometimes neural networks can catch signals that gradient boosting models cannot get, and can be excellent single models or models that shine in an ensemble.

As many Grandmasters of the present and the past often quote, mixing together diverse models (such as a neural network and a gradient boosting model) always produces better results than single models taken separately in a tabular data problem. Owen Zhang, previously number one on Kaggle, discusses at length in the following interview how neural networks and GBMs can be blended nicely for better results in a competition:

Building a neural network quickly...