Sign in

Photo by Lucaxx Freire on Unsplash [1]


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 summarize the steps of “Kneedle”, demonstrate the benefits of this algorithm and its applications with the Python package “kneed”.


Daniel Kleine

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

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store