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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
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Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

Stacking

Stacking, or stacked generalization, is also called meta ensembling. It is a model ensembling technique that consists of combining data from multiple models' predictions and using them as features to generate a new model. The stacked model will most likely outperform each of the individual models due to the smoothing effect it adds, as well as due to its ability to "choose" the base model that performs best in certain scenarios. Keeping this in mind, stacking is usually most effective when each of the base models is significantly different from each other.

Stacking is widely used in real-world applications. One popular example comes from the well-known Netflix competition whose two top performers built solutions that were based on stacking models. Netflix is a well-known streaming platform and the competition was about building the best recommendation engine. The winning algorithm was based on feature-weighted-linear-stacking, which basically had meta features...

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