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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In this chapter, we discussed how ensembling multiple solutions works and proposed some basic code examples you can use to start building your own solutions. We started from the ideas that power model ensembles such as random forests and gradient boosting. Then, we moved on to explore the different ensembling approaches, from the simple averaging of test submissions to meta-modeling across multiple layers of stacked models.

As we discussed at the end, ensembling is more an art form based on some shared common practices. When we explored a successful complex stacking regime that won a Kaggle competition, we were amazed by how the combinations were tailored to the data and the problem itself. You cannot just take a stacking, replicate it on another problem, and hope that it will be the best solution. You can only follow guidelines and find the best solution consisting of averaging/stacking/blending of diverse models yourself, through lots of experimentation and computational...