If we pass the training variable and the testing variable into the mean squared error function, then won't those variables necessarily have different shapes because of the train_test_split function? If so, then how can we use the mean_squared_error function to evaluate the accuracy of our model? If I am misunderstanding anything, then please let me know. Any help would be much appreciated.
If you for example look at the mean squared error in the sklearn package, read the documentation:
sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
The inputs are not the training and testing variable, but the real test labels variable and the predicted test labels. These naturally have the same shape.