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I built a logistic regression model to classify a corpus of documents. The dependent variable is the type of document (eg A or B) while the dependent variables, because of dimensionality, are the first 2 components obtained by performing a Principal Component Analysis (PCA) (or a Single Value Decomposition (SVD)) on the columns (terms) of the document/term matrix.

The question is: on a new corpus of documents (and therefore a different document/terms matrix), is it methodologically correct to use the same model if the first 2 components are obtained from a different set of variables (terms)?

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Nope. Should instead use the transform matrix obtained from the first dataset.

e.g.

transformer = PCA.fit(data_train)
PCA_train = transformer.transform(data_train)
PCA_test = transformer.transform(data_test)
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  • $\begingroup$ Thank you very much, Kotri! Unfortunately, the two datasets do not have the same variables... $\endgroup$ – Alfredo Dec 11 '19 at 17:30

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