This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.

If you want to know more about the theory of each of these models, I recommend you take a look at the blog: Ensemble Learning: Stacking, Blending & Votingr

• Files
• How to use
• Contributing
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• License

• stacking.py: Contains an example from scratch about Stacked Generalization method
• blending.py: Contains an example from scratch about Blending, a variation from Stacked Generalization method.
• voting.py: Contains an example about Voting method by using the scikit-learn module.

For running stacking.py:

python stacking.py

For running blending.py:

python blending.py

For running voting.py:

python voting.py

however, I recommend you to work with a virtual environment, in this case I am using pipenv. So in order to install the dependencies located in the Pipfile you just need to type:

pipenv install

and then

pipenv shell

Feel free to fork the model and add your own suggestiongs.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/YourGreatFeature)
3. Commit your Changes (git commit -m 'Add some YourGreatFeature')
4. Push to the Branch (git push origin feature/YourGreatFeature)
5. Open a Pull Request

If you have any question, feel free to reach me out at:

• Twitter
• Medium
• Linkedin
• Email: [email protected]

Distributed under the MIT License. See LICENSE.md for more information.