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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Setting a random state for reproducibility

Before we start discussing the steps and models you may use in a tabular competition, it will be useful to return to the theme of reproducibility we mentioned above.

In most of the commands in the code you see on Kaggle Notebooks, you will find a parameter declaring a number, a seed, as the random state. This setting is important for the reproducibility of your results. Since many algorithms are not deterministic but are based on randomness, by setting a seed you influence the behavior of the random generator, making it predictable in its randomness: the same random seed corresponds to the same sequence of random numbers. In other words, it allows you to obtain the same results after every run of the same code.

That is why you find a random seed setting parameter in all machine learning algorithms in Scikit-learn as well as in all Scikit-learn-compatible models (for instance, XGBoost, LightGBM, and CatBoost, to name the most popular...