Making sense of

ensemble learning techniques

(Excerpt) 

For many companies/data scientists that specialize or work with machine learning (ML), ensemble learning methods have become the weapons of choice. As ensemble learning methods combine multiple base models, together they have a greater ability to produce a much more accurate ML model. For example, at Bigabid we’ve been ensemble learning to successfully solve a variety of problems ranging from optimizing LTV (Customer Lifetime Value) to fraud detection.

It is hard not overstate the importance of ensemble learning to the overall ML process, including the bias-variance tradeoff and the three main ensemble techniques: bagging, boosting and stacking. These powerful techniques should be a part of any data scientist’s tool kit, as they are concepts that are encountered everywhere. Plus, understanding their underlying mechanism is at the heart of the field of machine learning.

Read more from Ido Zehori in Targeting High LTV Users. 

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