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The Kaggle Book

The Kaggle Book

By : Konrad Banachewicz, Luca Massaron
4.1 (34)
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The Kaggle Book

The Kaggle Book

4.1 (34)
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)
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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

A brief introduction to ensemble algorithms

The idea that ensembles of models can outperform single ones is not a recent one. We can trace it back to Sir Francis Galton, who was alive in Victorian Britain. He figured out that, in order to guess the weight of an ox at a county fair, it was more useful to take an average from a host of more or less educated estimates from a crowd than having a single carefully devised estimate from an expert.

In 1996, Leo Breiman formalized the idea of using multiple models combined into a more predictive one by illustrating the bagging technique (also called the “bootstrap aggregating” procedure) that later led to the development of the even more effective random forests algorithms. In the period that followed, other ensembling techniques such as gradient boosting and stacking were also presented, thus completing the range of ensemble methods that we use today.

You can refer to a few articles to figure out how these ensembling...

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