Definition
Overfitting occurs when a machine learning model learns the training data too well, including its noise and specific nuances, to the point where it performs poorly on new, unseen data. This happens because the model has become too complex and has essentially memorized the training examples rather than generalizing underlying patterns. On the job, an overfit model can lead to unreliable predictions and flawed decision-making, wasting resources and potentially causing significant errors. For instance, a spam filter that overfits might incorrectly flag legitimate emails as spam because it learned specific phrases from past spam examples that are not universally indicative of spam. This is distinct from underfitting, where a model is too simple and fails to capture the essential patterns even in the training data. Overfitting is a critical concept tested in AI and machine learning certification domains.