What characterizes underfitting in a model?

Underfitting occurs when a model is too simplistic to adequately capture the underlying patterns in the data. This can happen when the model lacks the necessary complexity to learn from the training data, resulting in poor performance on both the training and testing datasets. As a consequence, the predictions made by an underfitted model will often have high bias, leading to generalized assumptions about the data rather than accurately representing it.

For example, a linear model applied to a dataset with a quadratic relationship may fail to capture the complexity of the relationship, leading to underfitting. This is a critical issue because it means the model cannot generalize well to new, unseen data, ultimately affecting its accuracy and reliability in practical applications, such as in dentistry where accurate predictions or classifications of patient data are essential.

In contrast, if a model captures complex patterns well, it indicates the model is fitting the training data effectively, possibly leading to overfitting if it does too well in the training set without generalizing to the testing set. A model that performs equally well on both training and testing data would be considered well-fitted, while an excessively complex model might suffer from overfitting, where it learns noise in the training data as if it were a significant pattern rather than general