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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Basic optimization techniques

The core algorithms for hyperparameter optimization, found in the Scikit-learn package, are grid search and random search. Recently, the Scikit-learn contributors have also added the halving algorithm to improve the performances of both grid search and random search strategies.

In this section, we will discuss all these basic techniques. By mastering them, not only will you have effective optimization tools for some specific problems (for instance, SVMs are usually optimized by grid search) but you will also be familiar with the basics of how hyperparameter optimization works.

To start with, it is crucial to figure out what the necessary ingredients are:

  • A model whose hyperparameters have to be optimized
  • A search space containing the boundaries of the values to search between for each hyperparameter
  • A cross-validation scheme
  • An evaluation metric and its score function

All these elements come together in the...