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Results tagged with pca
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user 109699
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
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GLMM with many and highly correlated features
If we were talking about independent data, then possible solution would be, say PCA, and then building the prediction model with a GLM based on these PCA features (although I never tried something like … Can you do PCA (or any variable reduction technique) in 2-level data ? And if yes, can you point me out where to learn about it? …
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