How to use Learning Curves to Diagnose Machine Learning Model Performance

Last Updated on August 6, 2019

A learning curve is a plot of model learning performance over experience or time.

Learning curves are a widely used diagnostic tool in machine learning for algorithms that learn from a training dataset incrementally. The model can be evaluated on the training dataset and on a hold out validation dataset after each update during training and plots of the measured performance can created to show learning curves.

Reviewing learning curves of models during training can be used to diagnose problems with learning, such as an underfit or overfit model, as well as whether the training and validation datasets are suitably representative.

In this post, you will discover learning curves and how they can be used to diagnose the learning and generalization behavior of machine learning models, with example plots showing common learning problems.

After reading this post, you will know:

• Learning curves are plots that show changes in learning performance over time in terms of experience.
• Learning curves of model performance on the train and validation datasets can be used to diagnose an underfit, overfit, or well-fit model.
• Learning curves of model performance can be used to diagnose whether the train or
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