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Learning curve (machine learning)
From Wikipedia, the free encyclopedia
In machine learning, a learning curve (or training curve) plots the optimal value of a model's loss function for a training set against this loss function evaluated on a validation data set with same parameters as produced the optimal function.[1] Synonyms include error curve, experience curve, improvement curve and generalization curve.[2]
![]() | This article provides insufficient context for those unfamiliar with the subject. (March 2019) |
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More abstractly, the learning curve is a curve of (learning effort)-(predictive performance), where usually learning effort means number of training samples and predictive performance means accuracy on testing samples.[3]
The machine learning curve is useful for many purposes including comparing different algorithms,[4] choosing model parameters during design,[5] adjusting optimization to improve convergence, and determining the amount of data used for training.[6]