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

Optimizing evaluation metrics

Summing up what we have discussed so far, an objective function is a function inside your learning algorithm that measures how well the algorithm’s internal model is fitting the provided data. The objective function also provides feedback to the algorithm in order for it to improve its fit across successive iterations. Clearly, since the entire algorithm’s efforts are recruited to perform well based on the objective function, if the Kaggle evaluation metric perfectly matches the objective function of your algorithm, you will get the best results.

Unfortunately, this is not frequently the case. Often, the evaluation metric provided can only be approximated by existing objective functions. Getting a good approximation, or striving to get your predictions performing better with respect to the evaluation criteria, is the secret to performing well in Kaggle competitions. When your objective function does not match your evaluation metric,...