Definition
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data. It fails to learn the relationships between the input features and the target variable, resulting in poor performance on both the training and test datasets. In practice, an underfit model will exhibit high bias, meaning its predictions are consistently far from the true values. This is problematic because it indicates the model is not generalizing well and cannot make accurate predictions on new, unseen data. For example, using a linear regression model to predict highly non-linear data would likely result in underfitting. Underfitting is distinct from overfitting, where a model is too complex and learns the training data noise, leading to poor generalization. This concept is relevant for professionals preparing for certifications like AWS Certified Solutions Architect Associate or PMP.