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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Blending models using a meta-model

The Netflix competition (which we discussed at length in Chapter 1) didn’t just demonstrate that averaging would be advantageous for difficult problems in a data science competition; it also brought about the idea that you can use a model to average your models’ results more effectively. The winning team, BigChaos, in their paper (Töscher, A., Jahrer, M., and Bell, R.M. The BigChaos Solution to the Netflix Grand Prize. Netflix prize documentation – 2009) made many mentions of blending, and provided many hints about its effectiveness and the way it works.

In a few words, blending is kind of a weighted averaging procedure where the weights used to combine the predictions are estimated by way of a holdout set and a meta-model trained on it. A meta-model is simply a machine learning algorithm that learns from the output of other machine learning models. Usually, a meta-learner is a linear model (but sometimes it can also...