In
statistics and
machine learning,
overfitting occurs when a
statistical model describes
random error or noise instead of the underlying relationship. Overfitting generally occurs when a model is excessively complex, such as having too many
parameters relative to the number of observations. A model that has been overfit will generally have poor
predictive performance, as it can exaggerate minor fluctuations in the data.