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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

Ensembling decision trees – random forests

The ensemble technique of bagging (which stands for bootstrap aggregating), which I briefly mentioned in Chapter 1, Getting Started with Machine Learning and Python, can effectively overcome overfitting. To recap, different sets of training samples are randomly drawn with replacements from the original training data; each resulting set is used to fit an individual classification model. The results of these separately trained models are then combined together through a majority vote to make the final decision.

Tree bagging, as described in the preceding paragraph, reduces the high variance that a decision tree model suffers from and, hence, in general, performs better than a single tree. However, in some cases, where one or more features are strong indicators, individual trees are constructed largely based on these features and, as a result, become highly correlated. Aggregating multiple correlated trees will not make much difference...

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