File:Ensemble Methods (51222899322).jpg

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Ensemble Methods help to create multiple models and then combine them to produce improved results: 1. BAGGing (Bootstrap AGGregating) reduces the high variance of the model. 2. Boosting makes out a stronger learner model from weak learner models by averaging their weights. 3. Stacking combines multiple classifications or regression techniques using a meta-classifier or meta-model.

Part of Geeklendar 2021 <a href="https://exeypanteleev.com/geeklendar" rel="noreferrer nofollow">exeypanteleev.com/geeklendar</a>
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Source Ensemble Methods
Author Exey Panteleev from Moscow, Russia

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This file is licensed under the Creative Commons Attribution 2.0 Generic license.
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This image was originally posted to Flickr by Exey Panteleev at https://flickr.com/photos/79275080@N00/51222899322. It was reviewed on 9 June 2021 by FlickreviewR 2 and was confirmed to be licensed under the terms of the cc-by-2.0.

9 June 2021

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current00:02, 9 June 2021Thumbnail for version as of 00:02, 9 June 20211,600 × 1,600 (1.69 MB)Tm (talk | contribs)Transferred from Flickr via #flickr2commons

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