Why does underfitting matter?#
Recognising underfitting helps your technical team diagnose why an AI model isn't delivering the expected accuracy or value. It signals that the model needs to be made more sophisticated or trained on more relevant data to become a useful business tool.
What causes underfitting?#
Imagine training a new employee on a complex approval process by only giving them one simple rule. For example, telling them "approve all invoices under $100." This employee will fail to handle the real-world complexity, such as knowing that certain vendors always require a manager's review, regardless of the amount. The employee's understanding (the model) is too basic for the job.
Technically, an underfitting model has not learned the true relationship between the inputs and the outputs in the data. Because its internal logic is not complex enough to represent the actual business trend, its conclusions are consistently wrong when tested on both historical and new information.
How is underfitting different from overfitting?#
Underfitting describes a model that is too simple and performs poorly on all data. Its direct opposite is overfitting, where a model becomes too complex and learns the training data too perfectly, including irrelevant details and noise. An overfit model looks great on historical data but fails when it encounters new, real-world situations, while an underfit model fails everywhere.