overfit
7.9
Made to memorize the training data so it does not work well on new data
- adjective
- /ˈoʊvərˌfɪt/
- Specialized
:
sobreajustado
- If a model is overfit, it may perform well on the training data but poorly on real-world examples.
And many users might try for months without staking anything at all to see if their model works on the real life data right and is not overfit.
- And many users might try for months without staking anything at all to see if their model works on the real life data right and is not overfit.
Examples
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The algorithm was so overfit that it failed to predict results on the test set.
-
You know, hopefully this denim jacket will add a little bit overfit element to the look.
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When creating machine learning models, you should avoid making them overfit to the training data.
Antonyms
How Well
overfit
- Specialized
fitted
2.0
Surface Forms
Etymology
The adjective overfit uses the same parts, over- ('too much') and fit ('matched'), so an overfit model is one that matches the data it learned from too exactly and therefore does badly on new examples.