A long-tailed distribution is a type of heavy-tailed distribution that has a tail (or tails) that drop off gradually and asymptotically.
Three examples of relevant phenomena that have long tails:
Frequencies of languages spoken
Population of cities
Pageviews of articles
All of these follow something close to 80-20 rule: 80% of outcomes (or outputs) result from 20% of all causes (or inputs) for any given event. This 20% forms the long tail in the distribution.
It’s important to be mindful of long-tailed distributions in classification and regression problems because the least frequently occurring values make up the majority of the population. This can ultimately change the way that you deal with outliers, and it also conflicts with some machine learning techniques with the assumption that the data is normally distributed. Alt_text
