The Cardinal Sin of Data Mining and Data Science: Overfitting


See on Scoop.itWhat is Data Science

Carla Gentry CSPO‘s insight:

We note that Overfitting is not the same as another major data science mistake – "confusing correlation and causation". The difference is that overfitting finds something where there is nothing. In case of "correlation and causation", researchers can find a genuine novel correlation and only discover a cause much later (see a great example from astronomy in Kirk D. Borne interview on Big Data in Astrophysics and Correlation vs. Causality).

See on www.kdnuggets.com

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