What is Underfitting?

Underfitting occurs when a model or algorithm fails to capture the complexity of the underlying data. Underfitting may happen in machine learning when the model or algorithm is too simple for the underlying data trends, like a linear model for a non-linear problem, or one that works well in a limited range of values but has poor predictive accuracy outside that range.


Why is Underfitting Important?

Underfitting means that the model development process did not use the right algorithm or model to learn from the training data and create a fit that accurately predicts future data results, or it did not have a sufficiently representative range of training data. Underfitting can be reduced by:

  • Selecting the right features to evaluate
  • Trying alternative approaches
  • Validating against a properly selected data set independent of the training data
  • Continuously evaluating models following deployment


How C3.ai Enables Organizations to Avoid Underfitting

C3.ai provides a rich machine learning development environment, the C3.ai ML Studio, as part of the C3 AI Suite. ML Studio enables data scientists to develop, train, test, deploy, and operate ML models at scale. Functions like “experiment” and “model management” make it easy to avoid or correct for underfitting during each phase of the development process, and to continually monitor the performance of deployed models to maintain and maximize accuracy over time.