Can you do PCA with categorical variables?
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.
What is PCA does it apply to categorical data or continuous data?
It is not recommended to use PCA when dealing with Categorical Data. So, the data has been represented as a matrix with rows as binary vectors where 1 means the user commented on this book type and 0 means he has not.
Is PCA only for continuous variables?
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. So yes, you can use PCA.
When to use PCA in a categorical dataset?
I believe that the variance in my dataset can be almost entirely described by the single categorical variable and one of the many continuous variables. To justify this, I would be interested in using PCA, but I’m not sure the best approach to use when I am considering categorical data.
Why is PCA desinged for continuous variables in Python?
PCA is desinged for continuous variables. It tries to minimize variance (=squared deviations). The concept of squared deviations breaks down when you have binary variables.
Is it good to use PCA on binary data?
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 desinged for continuous variables. It tries to minimize variance (=squared deviations).
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.