New to scikit-learn. I am using v 20.2. I am developing PLS regression models.I would like to know how important each of the original predictors/descriptors are in predicting the response. The diffferent matrices returned by scikit-learn for the learned PLS model(X_loadings, X_weights,etc) are giving descriptor-related values for each PLS component. But I am looking for a way to calculate/visualize the overall importance/contribution of each feature in the model. Can someone help me out here? Also, what of the matrices shows me the coefficient assigned to each PLS component in the final linear model?