Correct Answer: I and III

Dimensionality reduction creates new combinations of features that are uncorrelated. Feature engineering attempts to produce good features that describe the structures inherent in the dataset. In feature selection, however, we try to find a subset of the original set of variables, or features, to get a smaller subset which can be used to model the problem. This becomes even more important when the number of features are very large. It improves the accuracy of a model if the right subset is chosen.