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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Key parameters and how to use them

The next problem is using the right set of hyperparameters for each kind of model you use. In particular, in order to be efficient in your optimization, you need to know the values of each hyperparameter that it actually makes sense to test for each distinct algorithm.

In this section, we will examine the most common models used in Kaggle competitions, especially the tabular ones, and discuss the hyperparameters you need to tune in order to obtain the best results. We will distinguish between classical machine learning models and gradient boosting models (which are much more demanding in terms of their space of parameters) for generic tabular data problems.

As for neural networks, we can give you an idea about specific parameters to tune when we present the standard models (for instance, the TabNet neural model has some specific parameters to set so that it works properly). However, most of the optimization on deep neural networks in Kaggle...