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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Metrics for object detection problems

In recent years, deep learning competitions have become more and more common on Kaggle. Most of these competitions, focused on image recognition or on natural language processing tasks, have not required the use of evaluation metrics much different from the ones we have explored up to now. However, a couple of specific problems have required some special metric to be evaluated correctly: those relating to object detection and segmentation.

Figure 5.4: Computer vision tasks. (Source:,

In object detection, you don’t have to classify an image, but instead find relevant portions of a picture and label them accordingly. For instance, in Figure 5.4, an object detection classifier has been entrusted to locate within a photo the portions of the picture where either dogs or cats are present and classify each of them with a proper label. The example...