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Can you do PCA with categorical data?
While it is technically possible to use PCA on discrete variables, or categorical variables that have been one hot encoded variables, you should not. Simply put, if your variables don’t belong on a coordinate plane, then do not apply PCA to them.
Can I use PCA for binary variables?
While you can use PCA on binary data (e.g. one-hot encoded data) that does not mean it is a good thing, or it will work very well. PCA is designed for continuous variables. It tries to minimize variance (=squared deviations). The concept of squared deviations breaks down when you have binary variables.
When to use categorical PCA in data reduction?
Categorical principal components analysis (CATPCA) is appropriate for data reduction when variables are categorical (e.g. ordinal) and the researcher is concerned with identifying the underlying components of a set of variables (or items) while maximizing the amount of variance accounted for in those items (by the principal components).
Which is less meaningful MCA or PCA for categorical features?
It even is a least-squared output: it’s not as if PCA would segfault on such data. It works, but it is just much less meaningful than you’d want it to be; and supposedly less meaningful than e.g. frequent pattern mining. MCA is a known technique for categorical data dimension reduction.
Do you need PCA for categorical factor analysis?
Firstly Rules Known in conventional ,i.e. continuous, factor analysis apply to Categorical Factor Analyses as well.
Can you use PCA for categorical features in Python?
So yes, you can use PCA. And yes, you get an output. It even is a least-squared output – it’s not as if PCA would segfault on such data. It works, but it is just much less meaningful than you’d want it to be; and supposedly less meaningful than e.g. frequent pattern mining.