- Published on November 18, 2019
- In AI Features
What Are The ‘No Free Lunch Theorems’ In Data Science?
- By Kshiti Ballal
No Free Lunch Theorems (NFLTs): Two well-known theorems bearing the same name: One for supervised machine learning (Wolpert 1996) and one for search/optimization (Wolpert and Macready 1997).
The thing that they share in common is that they state that certain classes of algorithms have no "best" algorithm because on average, they'll all perform about the same.
Mathematically put, the computational cost of finding a solution, averaged over all problems in the class, is the same for any solution method. No solution, therefore, offers a short cut.
Today’s menu:
InceptionNFL real-world implicationsExample problemImplementationTakeawaysReferences
Once Upon A Time
The No Free Lunch Theorem (NFLT) is named after the phrase, there ain’t no such thing as a free lunch.
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Kshiti Ballal
Kshiti is a part of the AIM Writers Programme. She is an engineer with a newly-found passion for writing. Kshiti also has a profound interest in playing with the data, coupled with a strong desire to keeping pace with the IT trends.
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