What is underfitting in AI?

Underfitting in AI refers to a model that is too simple to capture the underlying structure of the data. This occurs when the model has insufficient complexity or when it has not been trained long enough. As a result, the model performs poorly on both training and validation data, failing to capture important patterns and relationships in the data.

How can underfitting be addressed?

Underfitting can be addressed by increasing the complexity of the model, either by adding more features, using a more complex model architecture, or training the model for a longer period. Regularization techniques, which constrain the model's complexity to prevent overfitting, can also contribute to underfitting if applied too heavily, so adjusting these can also help.

It's important to monitor the model's performance on both training and validation data to detect underfitting.

What are the implications of underfitting?

Underfitting can lead to inaccurate predictions or classifications, as the model fails to capture the underlying patterns in the data. This can have significant implications in applications where accurate predictions are critical, such as in healthcare or finance.