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Ensemble Machine Learning Cookbook

You're reading from   Ensemble Machine Learning Cookbook Over 35 practical recipes to explore ensemble machine learning techniques using Python

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136609
Length 336 pages
Edition 1st Edition
Languages
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Authors (2):
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Vijayalakshmi Natarajan Vijayalakshmi Natarajan
Author Profile Icon Vijayalakshmi Natarajan
Vijayalakshmi Natarajan
Dipayan Sarkar Dipayan Sarkar
Author Profile Icon Dipayan Sarkar
Dipayan Sarkar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Get Closer to Your Data 2. Getting Started with Ensemble Machine Learning FREE CHAPTER 3. Resampling Methods 4. Statistical and Machine Learning Algorithms 5. Bag the Models with Bagging 6. When in Doubt, Use Random Forests 7. Boosting Model Performance with Boosting 8. Blend It with Stacking 9. Homogeneous Ensembles Using Keras 10. Heterogeneous Ensemble Classifiers Using H2O 11. Heterogeneous Ensemble for Text Classification Using NLP 12. Homogenous Ensemble for Multiclass Classification Using Keras 13. Other Books You May Enjoy

Understanding stacked generalization

Stacked generalization is an ensemble of a diverse group of models that introduces the concept of a meta-learner. A meta-learner is a second-level machine learning algorithm that learns from an optimal combination of base learners:

"Stacked generalization is a means of non-linearly combining generalizers to make a new generalizer, to try to optimally integrate what each of the original generalizers has to say about the learning set. The more each generalizer has to say (which isn't duplicated in what the other generalizers have to say), the better the resultant stacked generalization."
- Wolpert (1992), Stacked Generalization

The steps for stacking are as follows:

  1. Split your dataset into a training set and a testing set.
  2. Train several base learners on the training set.
  3. Apply the base learners on the testing set to make predictions...
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