# 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) |

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]}