Overfitting means a model memorises its training examples and performs poorly on new cases.
Example
A model recognises every training photo perfectly but fails on photos with different lighting.
For pros
You can spot it when error on test data rises while error on training data falls. Remedies are more and more varied data, regularisation and stopping earlier. Benchmarks can also be “overfitted” when test questions leak into training data.
