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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 FREE CHAPTER
2. A Machine Learning Refresher 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

Creating the ensemble

In order to create the ensemble, we will utilize the openensembles library that we presented in Chapter 8, Clustering. As our dataset does not contain labels, we cannot use the homogeneity score in order to evaluate our clustering models. Instead, we will use the silhouette score, which evaluates how cohesive each cluster is and how separate different clusters are. First, we must load our dataset, which is provided in the WHR.csv file. The second file that we load, Regions.csv, contains the region that each country belongs to. We will utilize the data from 2017, as 2018 has a lot of missing data (for example, Delivery quality and Democratic quality are completely absent). We will fill any missing data using the median of the dataset. For our experiment, we will utilize the factors we presented earlier. We store them in the columns variable, for ease of reference...

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