Underfitting occurs when the machine learning model is not well-tuned to the training set. The resulting model is not capturing the relationship between input and output well enough. Therefore, it doesn’t produce accurate predictions, even for the training dataset. Resultingly, an underfitted model generates poor results that lead to high-error decisions, like an overfitted model.

An underfitted model is not complex enough to recognize the patterns in the dataset. Usually, it has a high bias towards one output value. This is because it considers the variations of the input data as noise and generates similar outputs regardless of the given input.

When training a model, we want it to fit well to the training data. Still, we want it to generalize and generate accurate predictions for unseen data, as well. As a result, we don’t want the resulting model to be on any extreme.