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

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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

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 a bonus section in which we implemented a GBT model with XGBoost. Bagging...