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Python Machine Learning By Example

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

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835085622
Length 518 pages
Edition 4th Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (18) Chapters Close

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

Handling multiclass classification

One last thing worth noting is how logistic regression algorithms deal with multiclass classification. Although we interact with the scikit-learn classifiers in multiclass cases the same way as in binary cases, it is useful to understand how logistic regression works in multiclass classification.

Logistic regression for more than two classes is also called multinomial logistic regression, better known latterly as softmax regression. As you have seen in the binary case, the model is represented by one weight vector w, and the probability of the target being 1 or the positive class is written as follows:

In the K class case, the model is represented by K weight vectors, w1, w2, ..., wK, and the probability of the target being class k is written as follows:

See the following term:

The preceding term normalizes the following probabilities (k from 1 to K) so that they total 1:

The cost function in the binary case...

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