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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 FREE CHAPTER 2. Getting Started with Ensemble Machine Learning 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

An ensemble of homogeneous models for handwritten digit classification

In this example, we will use a dataset called The Street View House Numbers (SVHN) dataset from http://ufldl.stanford.edu/housenumbers/. The dataset is also provided in the GitHub in .hd5f format.

This dataset is a real-world dataset and is obtained from house numbers in Google Street View images.

We use Google Colab to train our models. In the first phase, we build a single model using Keras. In the second phase, we ensemble multiple homogeneous models and ensemble the results.

Getting ready

The dataset has 60,000 house number images. Each image is labeled between 1 and 10. Digit 1 is labelled as 1, digit 9 is labelled as 9, and digit 0 is labelled as...

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