What is an outcome of underfitting a machine learning model?

Underfitting occurs when a machine learning model is too simplistic to capture the underlying patterns in the dataset. As a result, the model fails to learn enough from the training data, which leads to poor performance when making predictions. This is characterized by making inaccurate outputs not only on training data but also on testing data, as the model has not learned the necessary relationships within the data.

In contrast, accurately predicting outcomes on both training and testing datasets suggests that the model is well-fitted or even potentially overfitted. Capturing complex relationships typically indicates a more sophisticated model that is adequately trained, while high model confidence and clarity in predictions are associated with well-tuned models that balance bias and variance effectively. Therefore, the outcome of underfitting distinctly aligns with poor predictive ability and the generation of inaccurate outputs, making this the correct choice.