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Photo by Lucaxx Freire on Unsplash [1]

Theory

When working with data, it is sometimes important to know where a data point’s relative costs to increase some tunable parameter is no longer worth the corresponding performance benefit. The algorithm “kneedle” detects those beneficial data points showing the best balance inherent tradeoffs — called “knees” (curves that have negative concavity) or sometimes “elbows” (curves that have positive concavity) — in discrete data sets based on the mathematical definition of curvature for continuous functions. With this article, I want to demonstrate the benefits of this algorithm and its applications with the Python package “kneed”.

The “kneedle” algorithm has been published…

Daniel Kleine

Industrial and organizational psychologist specialized in the fields of Data Analytics and Data Science with a focus on Machine Learning

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