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In order to choose the best number of underlying factors for my data using factor analysis, I decided to use the tutorial outlined in scikit-learn's documentation.
Running cross_val_score(fa, X) outputs a score (usually a negative number). What is this score actually measuring? Any references with your answer would be much appreciated!
From this thread on data science stack exchange where I asked the same question:
FactorAnalysis performs a maximum likelihood estimate of the so-called loading matrix, the transformation of the latent variables to the observed ones, using expectation-maximization (EM).
