Hi Khuyen Tran,
Ok so I checked the documentation of YellowBrick. And I think there is a misunderstanding. Rank1D does not assess the feature importance relative to the target. As quoted from the documentation :
"A one-dimensional ranking of features utilizes a ranking algorithm that takes into account only a single feature at a time (e.g. histogram analysis). By default we utilize the Shapiro-Wilk algorithm to assess the normality of the distribution of instances with respect to the feature."
It only assess the normality of the features with respect to the target. So I guess this is usefull in a scenario when you want to assess the validity of the hypotheses of your linear regression model. But having a feature normaly distributed with respect to the target is not sufficient (at all) to make it a good predictor :-)
I would recommend using Rank2D that computes the pearson correlation between pair of features and check the correlation with the target instead.