I have just started machine learning and was asked this concept-based question,
"Suppose you are working on a stock market prediction model and the data you collected have millions of features, what should you do?"
I found two possible methods - Regularization and dimensionality reduction. But I was told that regularization is incorrect because it does not affect input data but only the output data. Whereas dimensionality reduction removes unnecessary/useless data that generates noise.
My main question is, if excessive features in a dataset could cause overfitting and regularization can help to reduce the complexity of the model, why is regularization not a valid solution?
Would sincerely appreciate if anyone could provide some usage examples for both methods.