Precision and Recall History

Data Science, AI and ML
#1

Precision and recall were first defined by the American scientist Allen Kent and his colleagues in their 1955 paper Machine literature searching VIII. Operational criteria for designing information retrieval systems .

Kent served in the US Army Air Corps in World War II, and was assigned after the war by the US military to a classified project at MIT in mechanized document encoding and search.

In 1955, Kent and his colleagues Madeline Berry, Fred Luehrs, and J.W. Perry were working on a project in information retrieval using punch cards and reel-to-reel tapes. The team found a need to be able to quantify the performance of an information retrieval system objectively, allowing improvements in a system to be measured consistently, and so they published their definition of precision and recall.

They described their ideas as a theory underlying the field of information retrieval, just as the second law of thermodynamics “underlies the design of a steam engine, regardless of its type or power rating”.

Since then, the definitions of precision and recall have remained fundamentally the same, although for search engines the definitions have been modified to take into account certain nuances of human behavior, giving rise to the modified metrics precision @ k and mean average precision, which are the values normally quoted in information retrieval contexts today.

In 1979 the Dutch computer science professor Cornelis Joost van Rijsbergen recognized the problems of defining search engine performance in terms of two numbers and decided on a convenient scalar function that combines the two. He called this metric the Effectiveness function and assigned it the letter E. This was later modified to the F-score, or Fβ score, which is still used today to summarize precision and recall.