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The video presenting the content for these slides and all the related materials including source code and sample data can be downloaded from this link: http://amsantac.co/blog/en/2016/10/22/model-stacking-classification-r.html.
Model ensembling comprises a set of methods that aims to increase accuracy by combining the predictions of multiple models together.
Ensemble methods can be categorized based on their approach for combining classifiers: one approach is to use similar classifiers and to combine them together using techniques such as bagging, boosting or random forests. A second approach is to combine different classifiers using model stacking.
In this presentation I provide an example of model stacking applied to the classification of a Landsat image.