Trining a neural network with leave one out crossval method

15 views (last 30 days)
Hello there,
I am new at neural networks and matlab. I am tring to train a network but i have less data available with me, so I am trying with leave-one-out method. But i am unable to find a way. Is there any direct method of training with leave-one-out training in matlab environment or what way should i follow. Thank you.

Answers (1)

Jalaj Gambhir
Jalaj Gambhir on 25 Feb 2020
Hi,
'Leave-one-out' is a cross validation method. You can generate cross validation indices for train and test set using cvpartition, specifying 'LeaveOut' parameter. This would generate partitions of n-1 training samples and 1 test sample.
>> load fisheriris;
>> x = meas;
>> y = species;
>> c = cvpartition(y,'LeaveOut')
This generates
c =
Leave-one-out cross validation partition
NumObservations: 150
NumTestSets: 150
TrainSize: 149 149 149 149 149 149 149 149 149 149 ...
TestSize: 1 1 1 1 1 1 1 1 1 1 ...
For each partition 'i', you can generate train and test samples as:
>> x_train = x(training(c,i),:);
>> y_train = y(training(c,i),:); % You might want to convert this to one-hot-encoded vectors
>> x_test = x(test(c,i),:);
>> y_test = y(test(c,2),:); % You might want to convert this to one-hot-encoded vectors
Then you can use this train and test data to train a neural network using tools like nnstart which are perfect for beginners. Look at an example here.
  1 Comment
Juan Manuel Miguel
Juan Manuel Miguel on 6 Aug 2020
Thank you Jalaj, it was very useful for me. I think you meant y_test = y(test(c,i),:); instead of y_test = y(test(c,2),:); didn't you?
Thank you

Sign in to comment.

Categories

Find more on Deep Learning with Time Series and Sequence Data in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by