What Is Overfitting?
Overfitting occurs when an AI model learns its training data so perfectly that it fails to make accurate predictions on new, real-world data. Businesses often find that an AI model that performed with near-perfect accuracy during development suddenly fails when deployed, leading to unreliable forecasts, recommendations, or decisions. Overfitting is the technical term for why this happens.
How it helps#
Understanding overfitting helps your technical team build more robust and reliable AI systems. It ensures the model has learned the general patterns in your data, not just the specific examples it was shown, so it can perform effectively and add value in a live business environment.
How it works#
When an AI model is being built, it learns from a set of data known as "training data." The goal is for the model to identify the underlying patterns and relationships within that data.
Overfitting happens when the model becomes too complex and begins to memorize the training data, including its random fluctuations and irrelevant details—often called "noise." Because it has essentially memorized the specific training examples instead of learning the general principles, it becomes brittle. When presented with new data that doesn't perfectly match what it has memorized, its performance drops significantly.
How it is different#
Overfitting is when a model is too complex and learns the training data's noise, making it perform poorly on new data. In contrast, underfitting is when a model is too simple to capture the underlying patterns in the data at all. A properly "fitted" model strikes the right balance, learning the general rules from the data without being overly influenced by its noise, allowing it to make useful predictions in a variety of situations.