What is most likely causing a higher Mean Squared Error (MSE) in more complex machine learning models?

A higher Mean Squared Error (MSE) in more complex machine learning models is often primarily attributed to overfitting to the training data. When a model becomes too complex, it may learn not only the underlying patterns in the training dataset but also the noise. This excessive fit to the training data results in a model that performs exceptionally well on that data but poorly on unseen data, thereby increasing the MSE on the validation or test sets.

Overfitting is characterized by a significant discrepancy between training performance and validation performance, leading to a high MSE when evaluating the model on new, unseen data. This balance illustrates why regularization techniques or cross-validation are often employed to mitigate overfitting and ensure that the model maintains its ability to generalize well.

The other options, while related to model performance, do not directly relate to the heightened MSE observed with complex models as effectively as overfitting does. For example, a low learning rate could slow down convergence but doesn't typically cause increased MSE; an incorrect loss function calculation could lead to errors but would not specifically tie to the complexity of the model, and underfitting is more associated with a model that is too simplistic rather than complex.