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

Predicting Online Ad Click-Through with Tree-Based Algorithms

We built a face image classifier in the previous chapter. In this chapter and the next, we will be solving one of the most data-driven problems in digital advertising: ad click-through prediction—given a user and the page they are visiting, this predicts how likely it is that they will click on a given ad. We will focus on learning tree-based algorithms (including decision tree, random forest, and boosted trees) and utilize them to tackle this billion-dollar problem. We will be exploring decision trees from the root to the leaves, as well as the aggregated version, a forest of trees. This won't be a theory-only chapter, as there are a lot of hand calculations and implementations of tree models from scratch included. We will be using scikit-learn and XGBoost, a popular Python package for tree-based algorithms.

We will cover the following topics in this chapter: