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

Exploring a decision tree from the root to the leaves

A decision tree is a tree-like graph, that is, a sequential diagram illustrating all of the possible decision alternatives and their corresponding outcomes. Starting from the root of a tree, every internal node represents the basis on which a decision is made. Each branch of a node represents how a choice may lead to the next node. And, finally, each terminal node, the leaf, represents the outcome produced.

For example, we have just made a couple of decisions that brought us to the point of using a decision tree to solve our advertising problem:

Figure 3.2: Using a decision tree to find the right algorithm

The first condition, or the root, is whether the feature type is numerical or categorical. Let’s assume our ad clickstream data contains mostly categorical features, so it goes to the right branch. In the next node, our work needs to be interpretable by non-technical clients, so, it goes to the right branch...