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Book Overview & Buying
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Table Of Contents
The Kaggle Book - Second Edition
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When you start competing on Kaggle, it doesn’t take long to realize that you cannot win with a single, well-devised model; you must ensemble multiple models. Next, you will immediately wonder how to set up a working ensemble. While there are a few guides available, much of the understanding still draws from Kaggle’s community insights rather than scientific papers.
The point here is that if ensembling is the key to winning in Kaggle competitions, in the real world, ensembling is instead associated with complexity, poor maintainability, difficult reproducibility, and hidden technical costs for little advantage. Often, the small boost that can move you from the lower ranks to the top of the leaderboard doesn’t matter for real-life applications because the costs overshadow the advantages. However, that doesn’t mean that ensembling is not being used at all in the real world. In a limited way, such as by averaging...