Learning Curve:
It is used for graphical visualization of the model evaluation process. It usually refers to a plot of the prediction accuracy/error vs. the training set size (i.e: how better does the model get at predicting the target as you the increasing number of instances used to train it)
When both the training and test/validation performance are plotted together then we can diagnose the bias-variance tradeoff (i.e determine if we benefit from adding more training data, and assess the model complexity by controlling regularization or number of features).
Hope this answer helps.
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