Importance of original features after applying PCA : <p>Assume that I have 5 features A, B, C, D, E.</p>

<p>I apply PCA to my data to reduce the dimensionality to 3. It means now I have 3 principal components where each PC is a new variable. Now I want to know which feature among A, B, C, D, E is most important, which is the 2nd most important feature and so on and by how much (such as in terms of score).</p>

<p>I know that the first PC has the highest variance(importance), followed by 2nd, followed by 3rd and so on but I want the importance among original features A, B, C, D, E. Is there any method to find it?</p>
