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

Metrics for multi-class classification

When moving to multi-class classification, you simply use the binary classification metrics that we have just seen, applied to each class, and then you summarize them using some of the averaging strategies that are commonly used for multi-class situations.

For instance, if you want to evaluate your solution based on the F1 score, you have three possible averaging choices:

  • Macro averaging: Simply calculate the F1 score for each class and then average all the results. In this way, each class will count as much the others, no matter how frequent its positive cases are or how important they are for your problem, resulting therefore in equal penalizations when the model doesn’t perform well with any class:

  • Micro averaging: This approach will sum all the contributions from each class to compute an aggregated F1 score. It results in no particular favor to or penalization of any class, since all the computations...