You're reading from Python Machine Learning By Example Unlock machine learning best practices with real-world use cases

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835085622

Length 518 pages

Edition 4th Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Author (1):

Yuxi (Hayden) Liu

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Table of Contents (18) Chapters

Preface

1. Getting Started with Machine Learning and Python

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

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

4. Predicting Online Ad Click-Through with Logistic Regression

5. Predicting Stock Prices with Regression Algorithms

6. Predicting Stock Prices with Artificial Neural Networks

7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

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

9. Recognizing Faces with Support Vector Machine

10. Machine Learning Best Practices

11. Categorizing Images of Clothing with Convolutional Neural Networks

12. Making Predictions with Sequences Using Recurrent Neural Networks

13. Advancing Language Understanding and Generation with the Transformer Models

14. Building an Image Search Engine Using CLIP: a Multimodal Approach

15. Making Decisions in Complex Environments with Reinforcement Learning

16. Other Books You May Enjoy

17. Index

Summary

In this chapter, we started with an introduction to a typical machine learning problem, online ad click-through prediction, and its inherent challenges, including categorical features. We then looked at tree-based algorithms that can take in both numerical and categorical features.

Next, we had an in-depth discussion about the decision tree algorithm: its mechanics, its different types, how to construct a tree, and two metrics (Gini Impurity and entropy) that measure the effectiveness of a split at a node. After constructing a tree by hand, we implemented the algorithm from scratch.

You also learned how to use the decision tree package from scikit-learn and applied it to predict the CTR. We continued to improve performance by adopting the feature-based random forest bagging algorithm. Finally, the chapter ended with several ways in which to tune a random forest model, along with two different ways of ensembling decision trees, random forest and GBT modeling. Bagging...