# PLS-DA on sklearn: correlated features

Is there a way to retrieve the groups of features that, jointly, show a high loading in each LV. I'm aware that I can retrieve this by digging into x_loadings_, but given that covariance within and between groups is maximized, I'm wondering if there is a way to retrieve the combinations of features rather than individual features?

TL;DR: How to get strongly correlated features? Can we define these by their fitting coefficients?

This is how I'm performing the PLS-DA

from sklearn.cross_decomposition import PLSRegression
plsr=PLSRegression(n_components=2, scale=True)
plsr_fit=plsr.fit(input_data, y)

#x scores
plsr_fit.x_scores_