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Hands-On Ensemble Learning with Python

You're reading from   Hands-On Ensemble Learning with Python Build highly optimized ensemble machine learning models using scikit-learn and Keras

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781789612851
Length 298 pages
Edition 1st Edition
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Authors (2):
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Konstantinos G. Margaritis Konstantinos G. Margaritis
Author Profile Icon Konstantinos G. Margaritis
Konstantinos G. Margaritis
George Kyriakides George Kyriakides
Author Profile Icon George Kyriakides
George Kyriakides
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Required Software Tools
2. A Machine Learning Refresher FREE CHAPTER 3. Getting Started with Ensemble Learning 4. Section 2: Non-Generative Methods
5. Voting 6. Stacking 7. Section 3: Generative Methods
8. Bagging 9. Boosting 10. Random Forests 11. Section 4: Clustering
12. Clustering 13. Section 5: Real World Applications
14. Classifying Fraudulent Transactions 15. Predicting Bitcoin Prices 16. Evaluating Sentiment on Twitter 17. Recommending Movies with Keras 18. Clustering World Happiness 19. Another Book You May Enjoy

Summary

In this chapter, we presented the World Happiness Report data, providing a description of the data's purpose, as well as describing the data's properties. In order to gain further insights into the data, we utilized cluster analysis, leveraging ensemble techniques. We used co-occurrence matrix linkage in order to combine the cluster assignments of different base clusters. We tested various setups, with different ensemble sizes and numbers of neighbors, in order to provide a k-NN ensemble. After identifying that a t-SNE decomposition with a K value of 10 and 20 base clusters can be utilized, we analyzed the cluster assignments. We found that countries reporting the same happiness levels can, in fact, have different profiles. The most unhappy countries were, on average, developing countries who have to overcome many problems, concerning both their economies, and...

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