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Testing Your AI
Introduction
Our Previous Approach
K-Fold Cross-Validation
Leave-One-Out Cross Validation (LOOCV)
See For Yourself
Which K Should You Pick?
Conclusion
Code with Python
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Glossary

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Testing Your AILeave-One-Out Cross Validation (LOOCV)

Reading time: ~5 min

How far can we push k? With 20 farms, the most folds we can make is 20 — one farm per fold. This is leave-one-out cross-validation (LOOCV): k = n. Each round trains on 19 farms and tests on the single farm left out (the amber ring). Complete both missions.

Each validation "exam" is now just , and each model learns from almost all the data. Leave-one-out gave an average error of about 0.42.

Note

LOOCV trains as many models as there are rows. For 20 farms that is nothing; for 1 lakh photos it means training 1 lakh models. So leave-one-out is used only on small datasets, or for models with a mathematical shortcut.

Sina