Interview question: What is white noise in machine learning?

Four and a half decades ago, when I was a telecommunications technician in the United States Air Force, I had to deal with the concept of white noise and how it degraded communications signals. Now in the 2020’s, after the explosion of data science, I have come across the term once again, but this time referring to datasets.

White noise is a statistical term used to describe a random signal that has a constant power spectral density. Put simply, white noise is a random signal that contains equal intensity at different frequencies, giving it a constant power throughout the given frequency band. White noise are variations in your data that cannot be explained by any regression model.

When I was studying statsmodels’ ARIMA model to forecast monthly temperatures in India, I came across the concept of white noise and how it can alter a time series forecast:-

Testing for white noise is one of the first things that a data scientist should do so as to avoid spending time on fitting models on data sets that offer no meaningfully extractable information.