I have a dataset that has some 25 continuous variables and a continuous target. A tutor showed using this dataset, that when PCA scores are used instead of raw variables as inputs for predicting target, the MAPE (mean absolute percentage error) came down significantly. My understand was that, it should not affect the accuracy, only the interpretation of variables.
Question: how does PCA improve the accuracy over the raw variables?