We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?

The correct answer is B. As the training set size increases, the mean training error will decrease. This is because a larger training set will provide more data for the linear regressor to learn from, which will allow it to make more accurate predictions.

Option A is incorrect because the mean training error should decrease as the training set size increases.

Option C is incorrect because the mean training error should not remain constant as the training set size increases.

Option D is incorrect because it is possible to say what will happen to the mean training error as the training set size increases.