Learning curve (machine learning)

In machine learning, a learning curve (or training curve) shows the validation and training score of an estimator for varying numbers of training samples. It is a tool to find out how much a machine learning model benefits from adding more training data and whether the estimator suffers more from a variance error or a bias error. If both the validation score and the training score converge to a value that is too low with increasing size of the training set, it will not benefit much from more training data.[1]

Learning curve showing training score and cross validation score

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The machine learning curve is useful for many purposes including comparing different algorithms,[2] choosing model parameters during design,[3] adjusting optimization to improve convergence, and determining the amount of data used for training.[4]

In the machine learning domain, there are two connotations of learning curves differing in the x-axis of the curves, with experience of the model graphed either as the number of training examples used for learning or the number of iterations used in training the model.[5]