Overfitting definition
Overfitting is a common problem in machine learning where a model learns the training data too precisely ( including its noise and outliers) instead of the general patterns. As a result, it performs exceptionally well on data it has already seen, but poorly on new or real-world data.
You can think of overfitting as a form of memorization rather than understanding. The model captures every detail of the training examples, even those that aren’t useful for prediction, which leads to inaccurate results when the context changes.
See also: underfitting, machine vision, training data, supervised machine learning, unsupervised machine learning
How overfitting happens
Overfitting usually occurs when:
- The training dataset is too small or lacks diversity.
- The model is too complex, with more parameters than necessary.
- The model is trained for too long without regularization or validation checks.
For example, a spam filter that memorizes the exact phrases in known spam emails may fail to recognize new ones with slightly different wording. In cybersecurity, an overfitted anomaly detection model might flag normal behavior as suspicious because it learned from a narrow dataset.
Common techniques to reduce overfitting include cross-validation, dropout, early stopping, and regularization — all of which help the model focus on meaningful patterns rather than noise.
Why overfitting matters
Overfitting reduces a model’s reliability and limits its use in real-world applications. Systems that rely on overfitted models can make poor decisions and generate false positives.. Ensuring that models generalize well to new data is essential for trustworthy AI. That is especially the case in cybersecurity, healthcare, and financial systems where errors carry real consequences.
