When increasing the model flexibility, what tends to happen to the training Mean Squared Error (MSE)?

When increasing model flexibility, the training Mean Squared Error (MSE) tends to decrease. This is because a more flexible model is able to fit the training data more closely, capturing patterns and variance present in the data. As the model's flexibility increases, it adapts more closely to the noise and specific characteristics of the training data. This often leads to a lower training MSE, as the model is essentially minimizing the error for that specific dataset.

However, it is important to note that while training MSE decreases, the model may become overly complex, leading to overfitting. In overfitting, although the training MSE is low, the performance on unseen data can deteriorate, resulting in increased generalization error. Hence, while training MSE drops with increased flexibility, the question specifically highlights its behavior during training, not on unseen or validation data.

The options mentioning infinite, increasing, or remaining unchanged do not accurately reflect the behavior of training MSE with increased model flexibility, as they overlook the fundamental relationship between model flexibility and its capacity to minimize training error.