K fold validation for feedforward net
조회 수: 6(최근 30일)
I have created a forwardnet and becuase I only have a handful of cases i need to use K fold validation. However, I am a bit confused on how to do that. I have seen crossval and cvpartition functions but i still do not know how to use them. can you help me train the network using kfold validation?
Gaurav Garg 2020년 12월 31일
cvparition partitions data for cross-validation. It defines a random partition on data set and uses it to define training and test sets for validating a statistical model.
tbl = array2table(X);
tbl.Y = Y;
rng('default') % For reproducibility
n = length(tbl.Y);
hpartition = cvpartition(n,'Holdout',0.3); % Nonstratified partition
hpartition holds the total number of observations, total number of test sets, the size of training set and the size of test size.
To display the indeces which are being used for training, you can use -
To display the indeces which are being used for testing, you can use -
While, using crossval, you can estimate the loss returned by 10-fold cross-validation error estimate.
err = crossval(criterion,X,y,'Predfun',predfun)
% returns a 10-fold cross-validation error estimate for the
% function predfun based on the specified criterion