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

View More author details

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

Estimating with decision tree regression

Decision tree regression is also called a regression tree. It is easy to understand a regression tree by comparing it with its sibling, the classification tree, which you are already familiar with. In this section, we will delve into employing decision tree algorithms for regression tasks.

Transitioning from classification trees to regression trees

In classification, a decision tree is constructed by recursive binary splitting and growing each node into left and right children. In each partition, it greedily searches for the most significant combination of features and its value as the optimal splitting point. The quality of separation is measured by the weighted purity of the labels of the two resulting children, specifically via Gini Impurity or Information Gain.

In regression, the tree construction process is almost identical to the classification one, with only two differences because the target becomes continuous:

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