Definition
A phenomenon where a machine learning model learns the training data and its noise too precisely, resulting in excellent performance on training data but poor generalization to new, unseen data.
In short: A phenomenon where a machine learning model learns the training data and its noise too precisely, resulting in excellent performance on training data but poor generalization to new, unseen data.