I'm doing Analytics Edge course from EDX. The course is using R while I'm using Python.
In linear regression, in order to improve the model, we have to figure out the most significant features.
The course is using the summary function in R to look at the dots signifying the importance of the feature and the p-values. No such thing exists in sklearn.
So I'm using coefficients to see the most significant features. But I'm not sure I should trust coefficients to select the most significant features (even though for this problem, they are in agreement)
So is coefficients from linearRegression in sklearn reliable in determining the significance of the features? Are p-values themselves reliable in detecting the significant features?
(I know of statsmodels and do not wish to use)