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

Implementing a decision tree from scratch

We develop the CART tree algorithm by hand on a toy dataset as follows:

Figure 3.8: An example of ad data

To begin with, we decide on the first splitting point, the root, by trying out all possible values for each of the two features. We utilize the weighted_impurity function we just defined to calculate the weighted Gini Impurity for each possible combination, as follows:

If we partition according to whether the user interest is tech, we have the 1^st, 5^th, and 6^th samples for one group and the remaining samples for another group. Then the classes for the first group are [1, 1, 0], and the classes for the second group are [0, 0, 0, 1]:

Gini(interest, tech) = weighted_impurity([[1, 1, 0], [0, 0, 0, 1]])
                     = 0.405

If we partition according to whether the user’s interest is fashion, we have the 2^nd and 3^rd samples for one group and the remaining samples for another group. Then the classes for...