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)
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

Denoising with autoencoders

Autoencoders, initially better known for non-linear data compression (a kind of non-linear PCA) and image denoising, started being recognized as an interesting tool for tabular competitions after Michael Jahrer (https://www.kaggle.com/mjahrer) successfully used them to win the Porto Seguro’s Safe Driver Prediction competition (https://www.kaggle.com/c/porto-seguro-safe-driver-prediction). Porto Seguro was a popular, insurance-based risk analysis competition (more than 5,000 participants) characterized by particularly noisy features.

Michael Jahrer describes how he found a better representation of the numeric data for subsequent neural net supervised learning by using denoising autoencoders (DAEs). A DAE can produce a new dataset with a huge number of features based on the activations of the hidden layers at the center of the network, as well as the activations of the middle layers encoding the information.

In his famous post (https://www...