Overfitting happens when a model learns the training data too closely, including noise, quirks, or accidental patterns that do not generalize. The model may look strong during training but perform poorly once it sees genuinely new data. In simple terms, it learned the examples instead of learning the broader pattern.
Why Overfitting Happens
Overfitting often appears when the model is too flexible for the amount or quality of data available, when evaluation is weak, or when the training examples are not representative of real use. It can also happen when teams tune repeatedly against the same benchmark until they unintentionally optimize for the test itself.
One sign of overfitting is a gap between training performance and performance on holdout data. The model seems excellent on familiar examples but loses accuracy, stability, or usefulness in the wild.
How Teams Reduce It
Teams reduce overfitting through better data, stronger evaluation splits, regularization, simpler models, early stopping, and careful monitoring after deployment. Sometimes the answer is not more complexity, but less. A smaller model with honest evaluation can outperform a more elaborate system that memorized the wrong things.
Overfitting is one of the most important concepts in machine learning because it reminds us that model quality is not about looking smart on known examples. It is about holding up under new conditions.
Related concepts: Machine Learning, Supervised Learning, Model Drift, and Bias.