In duration of reading about LinearDiscriminantAnalysis using python , I had got two different methods to implement it which are available here , http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis
In these method the signature is here ,
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(solver=’svd’, shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)
Now again i found one more method with same kind of signature , which is available here ,
sklearn.lda.LDA(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)
I just wanted to know that what is difference between both . which method we should use in projects and why ?