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
Other Books You May Enjoy


In this chapter, we discussed hyperparameter optimization at length as a way to increase your model’s performance and score higher on the leaderboard. We started by explaining the code functionalities of Scikit-learn, such as grid search and random search, as well as the newer halving algorithms.

Then, we progressed to Bayesian optimization and explored Scikit-optimize, KerasTuner, and finally Optuna. We spent more time discussing the direct modeling of the surrogate function by Gaussian processes and how to hack it, because it can allow you greater intuition and a more ad hoc solution. We recognize that, at the moment, Optuna has become a gold standard among Kagglers, for tabular competitions as well as for deep neural network ones, because of its speedier convergence to optimal parameters in the time allowed in a Kaggle Notebook.

However, if you want to stand out among the competition, you should strive to test solutions from other optimizers as well.