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Table Of Contents
Machine Learning For Dummies
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For the same reasons that averaging works, stacking can also provide you with better performance. In stacking, you build your machine learning models in two (or sometimes even more) stages. Initially this technique predicts multiple results using different algorithms, with all of them learning from the features present in your data. During the second phase, instead of providing features that a new model will learn, you provide that model with the predictions of the other, previously trained models.
Using a two-stage approach is justified when guessing complex target functions. You can approximate them only by using multiple models together and then by combining the result of the multiplication in a smart way. You can use a simple logistic regression or a complex tree ensemble as a second-stage model.
The Netflix competition provides evidence and a detailed illustration about how heterogeneous models can be stacked together to form more powerful models. However, implementing...
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