Dimensionality / noise reduction techniques for regression problems
<p>What are some techniques for dimensionality reduction in regression problems? I have tried the only unsupervised techniques I know, PCA and Kernel PCA (using the scikit learn library), but I have not seen any improvements using these. Perhaps these are only suitable for classification problems? What are some other techniques I can try? Preferably, ones that are implemented in sklearn.</p>