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 FREE CHAPTER

2. Building a Movie Recommendation Engine with Naïve Bayes

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

Classifying data with logistic regression

In the last chapter, we trained tree-based models only based on the first 300,000 samples out of 40 million. We did so simply because training a tree on a large dataset is extremely computationally expensive and time consuming. Since we are not limited to algorithms directly taking in categorical features thanks to one-hot encoding, we should turn to a new algorithm with high scalability for large datasets. As mentioned, logistic regression is one of the most, or perhaps the most, scalable classification algorithms.

Getting started with the logistic function

Let’s start with an introduction to the logistic function (which is more commonly referred to as the sigmoid function) as the algorithm’s core before we dive into the algorithm itself. It basically maps an input to an output of a value between 0 and 1, and is defined as follows: