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

Converting categorical features to numerical – one-hot encoding and ordinal encoding

In Chapter 3, Predicting Online Ad Click-Through with Tree-Based Algorithms, I mentioned how one-hot encoding transforms categorical features to numerical features in order to use them in the tree algorithms in scikit-learn and TensorFlow. If we transform categorical features into numerical ones using one-hot encoding, we don’t limit our choice of algorithms to the tree-based ones that can work with categorical features.

The simplest solution we can think of in terms of transforming a categorical feature with k possible values is to map it to a numerical feature with values from 1 to k. For example, [Tech, Fashion, Fashion, Sports, Tech, Tech, Sports] becomes [1, 2, 2, 3, 1, 1, 3]. However, this will impose an ordinal characteristic, such as Sports being greater than Tech, and a distance property, such as Sports being closer to Fashion than to Tech.

Instead, one-hot encoding...

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

Table of Contents (18) Chapters

Converting categorical features to numerical – one-hot encoding and ordinal encoding

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

Table of Contents (18) Chapters

Converting categorical features to numerical – one-hot encoding and ordinal encoding

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