The rest of the scenarios the algorithm works well apart from setting the shrinkage parameter.
In order to improve the accuracy of the algorithm I am using various techniques.
In the scikit document it says using shrinkage can improve accuracy.
I am using AT&T data sets provided by scikit-learn (fetch_olivetti_faces).
Is it because the number of features are large or the memory issue?
Currently I am using VM machine to the run the code.
Hardware Specifications of the machine are:
CPU: Core i5
Memory: 1 GB Ram
Storage: 20 GB SSD
OS: UBUNTU 14.4
This is the code I am running:
lda = LinearDiscriminantAnalysis(solver='lsqr',shrinkage='auto')
This is the error I am getting:
