You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Feature engineering on categorical variables with Spark

In this chapter, I have demonstrated how to build an ad click predictor that learns from massive click logs using Spark. Thus far, we have been using one-hot encoding to employ categorical inputs. In this section, we will talk about two popular feature engineering techniques: feature hashing and feature interaction.

Feature hashing is an alternative to one-hot encoding, while feature interaction is a variant of one-hot encoding. Feature engineering means generating new features based on domain knowledge or defined rules, in order to improve the learning performance achieved with the existing feature space.

Hashing categorical features

In machine learning, feature hashing (also called the hashing trick) is an efficient way to encode categorical features. It is based on hashing functions in computer science, which map data of variable sizes to data of a fixed (and usually smaller) size. It...