TutorialEnsemble

TutorialEnsemble

Tutorial: Increasing the Predictive Power of Your Machine Learning Models with Stacking Ensembles

Whoever accompanies competitions knows that one of the most important things is to know how to put together several models to create a powerful solution. Several people have already asked me, by e-mail or in the presentations I made, about ensembles. This is an important issue not only for competitions, but also for real cases where you want to extract as much performance as possible from the models.

Ensembles are sets of models that offer better performance than each model that composes it.

So in this article I want to exemplify the best way I know of creating ensembles: stacking. This is a method I have used in all competitions that have had good results.

Problem Reference : https://www.kaggle.com/c/house-prices-advanced-regression-techniques/

Dependencies

• numpy
• pandas
• itertools
• scikit-learn
• jupyter notebook

Notebook : <a href="HomePrices-English.ipynb">English |</a> <a href="HomePrices.ipynb">Portuguese</a>

Special thanks to <a href="https://github.com/girishkuniyal">@girishkuniyal</a> for translating the material to English.