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 this chapter, we have discussed evaluation metrics in Kaggle competitions. First, we explained how an evaluation metric can differ from an objective function. We also remarked on the differences between regression and classification problems. For each type of problem, we analyzed the most common metrics that you can find in a Kaggle competition.

After that, we discussed the metrics that have never previously been seen in a competition and that you won’t likely see again. Finally, we explored and studied different common metrics, giving examples of where they have been used in previous Kaggle competitions. We then proposed a few strategies for optimizing an evaluation metric. In particular, we recommended trying to code your own custom cost functions and provided suggestions on possible useful post-processing steps.

You should now have grasped the role of an evaluation metric in a Kaggle competition. You should also have a strategy to deal with every common...