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ShortCut: Stationarity Assumption and Concept Drift

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ShortCut: Stationarity Assumption and Concept Drift

In this article series named ShortCut, I share some interesting and intriguing information elucidating various subjects from the books I have read by making quotes. In this particular article, I wanted to share about terms that of data science and Machine Learning algorithms.

"Finally, the world changes and models don't. Implicit in the ML(Machine Learning) process of data set construction, model training, and model evaluation is the assumption that the future will be the same as the past. This assumption is known as the stationarity assumption: the processes or behaviors that are being modeled are stationary through time (i.e., they don't change). Data sets are intrinsically historic in the sense that data are representations of observations that were made in the past. So, in effect, ML algorithms search through the past for patterns that might generalize to the future. Obviously, this assumption doesn't always hold. Data scientists use the term concept drift to describe how a process or behavior can change, or drift, as time passes. This is why models go out of date and need to be retrained and why the CRISP-DM process includes the outer circle. Processes need to put in place post model deployment to ensure that a model has not gone stale, and when it has, it should be retrained(1)."

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Figure - 94.1

References

(1) D. Kelleher, John & Tierney, Brendan. "Machine Learning." Data Science.. Cambridge, MA: The MIT Press, 2018. 150. Print.

Figure - 94.1 https://www.researchgate.net/figure/Static-assumption-and-Naivety-toward-adversarial-activity-puts-ML-at-risk_fig3_325235071