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

Metrics for classification (label prediction and probability)

Having discussed the metrics for regression problems, we are going now to illustrate the metrics for classification problems, starting from the binary classification problems (when you have to predict between two classes), moving to the multi-class (when you have more than two classes), and then to the multi-label (when the classes overlap).

Accuracy

When analyzing the performance of a binary classifier, the most common and accessible metric that is used is accuracy. A misclassification error is when your model predicts the wrong class for an example. The accuracy is just the complement of the misclassification error and it can be calculated as the ratio between the number of correct numbers divided by the number of answers:

This metric has been used, for instance, in Cassava Leaf Disease Classification (https://www.kaggle.com/c/cassava-leaf-disease-classification) and Text Normalization Challenge...