Underfitting is a term used in machine learning to describe a situation where a model is too simple to capture the underlying patterns or relationships present in the training data. It occurs when a model is not able to learn from the data effectively, resulting in poor performance both on the training data and unseen or test data.

Characteristics of underfitting include:

• High Bias: Underfit models often exhibit high bias, meaning they oversimplify the underlying patterns in the data and make strong assumptions that lead to inadequate representation of the relationships between features and the target variable.

• Poor Performance on Training Data: The model performs poorly on the training data itself, resulting in high errors or low accuracy. It fails to capture the complexity of the data due to oversimplification.

• Poor Generalization to Test Data: Underfit models also perform poorly on new, unseen data (test data or validation data). This indicates that the model has not learned the patterns well and cannot generalize to new instances beyond the training set.

• Inability to Capture Complex Relationships: Underfitting occurs when a model is not complex enough to capture complex nonlinear relationships, resulting in too much bias and too little variance.

Underfitting can be caused by various reasons, such as using a too simplistic model (e.g., linear model for highly nonlinear data), insufficient training or not allowing the model to learn adequately (e.g., too few iterations or epochs in training a neural network), or insufficient features or information to capture the underlying patterns.

To mitigate underfitting, one can use more complex models, increase the number of features, gather more relevant data, or adjust hyperparameters to allow the model to learn more complex relationships. However, it's essential to strike a balance between complexity and simplicity to avoid both underfitting and overfitting, achieving a model that generalizes well to new, unseen data.