What type of error occurs when the chosen model is overly complex and captures noise instead of the underlying trend?

The scenario described pertains to overfitting, which occurs when a model becomes excessively complex and learns not only the underlying patterns in the training data but also the noise. This excessive complexity makes it highly sensitive to fluctuations in the data, leading to poor generalization on new, unseen data.

Overfitting typically manifests when a model has too many parameters relative to the amount of training data available, which allows it to precisely fit the training set, including its outliers and noise. As a result, while the model may perform exceptionally well on the training dataset, its performance significantly declines on validation or test datasets because it has not captured the generalizable trends but rather memorized idiosyncrasies of the training set.

In contrast, underfitting occurs when a model is too simple and fails to capture the underlying trends altogether. Bias error refers to the systematic error due to overly simplistic assumptions in the learning algorithm, while variance error relates to sensitivity to small fluctuations in the training set. Recognizing the distinction between these concepts is crucial for practitioners in data science, as it impacts the choice of models and their configurations.