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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Averaging models into an ensemble

In order to introduce the averaging ensembling technique better, let’s quickly revise all the strategies devised by Leo Breiman for ensembling. His work represented a milestone for ensembling strategies, and what he found out at the time still works fairly well in a wide range of problems.

Breiman explored all these possibilities in order to figure out if there was a way to reduce the variance of error in powerful models that tended to overfit the training data too much, such as decision trees.

Conceptually, he discovered that ensembling effectiveness was based on three elements: how we deal with the sampling of training cases, how we build the models, and, finally, how we combine the different models obtained.

As for the sampling, the approaches tested and found were:

  • Pasting, where a number of models are built using subsamples (sampling without replacements) of the examples (the data rows)
  • Bagging, where a number...