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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Basic types of tasks

Not all objective functions are suitable for all problems. From a general point of view, you’ll find two kinds of problems in Kaggle competitions: regression tasks and classification tasks. Recently, there have also been reinforcement learning (RL) tasks, but RL doesn’t use metrics for evaluation; instead, it relies on a ranking derived from direct match-ups against other competitors whose solutions are assumed to be as well-performing as yours (performing better in this match-up than your peers will raise your ranking, performing worse will lower it). Since RL doesn’t use metrics, we will keep on referring to the regression-classification dichotomy, though ordinal tasks, where you predict ordered labels represented by integer numbers, may elude such categorization and can be dealt with successfully either using a regression or classification approach.


Regression requires you to build a model that can predict a real number...