Machine Learning Prediction - why and when beginning with PCA?
<p>From a very general point of view, when you have a dataset X and want to predict a label Y, what is the purpose of beginning with a PCA (principal component analysis) first, and then doing the prediction itself (with logistic regression , or random forest or whatever) from both intuitive and theoretical reason ? In which case can this improve the quality of prediction ?
Thanks !</p>

<p>I assume you mean PCA-based dimensionality reduction. Low-variance data often, but not always, has little predictive power, so removing low-variance dimensions of your dataset can be an effective way of improving predictor running time. In cases where it raises the signal to noise ratio, it can even improve prediction quality. But this is just a heuristic and is not universally applicable.</p>

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