How does using Categorical variables justifies the principle of PCA ? How can we transform categorical variables using the characteristics of standard normal distribution ? It doesn't make sense to represent the categorical variables which have no position in geometry ? It could lead to loss of information .

Hi Swarntam! Thanks for reaching out! In this particular case, categorical variables are part of the data we're working on and we convert them into dummies in order to participate in the process of building principal components. Categorical data is not from a normal distribution since the categories don't have an order. Hope this helps but feel free to post another question if needed. Thank you. Best, Ivan

Follow this topic

Swarntam Saurav

Last answered:

13 May 2022

Posted on:

10 May 2022

Couldn't interpret Categorical variables

in Linear Algebra and Feature Selection / PCA Covariance Matrix in Jupyter – Analysis and Interpretation

1 answers ( 0 marked as helpful)

Ivan Manov

Posted on:

13 May 2022

Couldn't interpret Categorical variables

Submit an answer