Underfitting (Low Bias) is a Fitting Problem that happens when the model performs poorly on the training data. This is because the model is unable to capture the relationship between the input examples (often called ) and the target values (often called ). I.e. It can be caused by the fact that the model is simple for the training data or the data does not contains the things that you are trying to predict.


Underfitting means that the model is too simple and misses the patterns and relationships in the data, resulting in low performance.

Techniques to solve underfitting problem:

  • Use more complex models.
  • Select better Features.
  • Increase data volume.
  • Reduce the Regularization.
  • In ANNs increase epochs/training iterations.