Definition
Underfitting occurs when a statistical model or machine learning algorithm cannot approximate the function mapping inputs to outputs accurately. This usually happens when the model is too simple for the complexity of the data, such as using linear regression on non-linear data. It results in poor performance on both training and test datasets. To resolve underfitting, practitioners may increase model complexity, add more relevant features, reduce regularization, or train the model for more epochs until it learns the patterns effectively.
Summary
A condition where a machine learning model fails to capture the underlying trend of the training data.
Key Concepts
- Bias-Variance Tradeoff
- Model Complexity
- Training Error
- Feature Engineering
Use Cases
- Diagnosing poor model performance
- Adjusting hyperparameters
- Selecting appropriate algorithms