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

Bayesian optimization

Leaving behind grid search (feasible only when the space of experiments is limited), the usual choice for the practitioner is to apply random search optimization or try a Bayesian optimization (BO) technique, which requires a more complex setup.

Originally introduced in the paper Practical Bayesian optimization of machine learning algorithms by Snoek, J., Larochelle, H., and Adams, R. P. (http://export.arxiv.org/pdf/1206.2944), the key idea behind Bayesian optimization is that we optimize a proxy function (also called a surrogate function) rather than the true objective function (which grid search and random search both do). We do this if there are no gradients, if testing the true objective function is costly (if it is not, then we simply go for random search), and if the search space is noisy and complex enough.

Bayesian search balances exploration with exploitation. At the start, it explores randomly, thus training the surrogate function as it goes...