MATLAB: Cross validation in matlab

accuracycross-validationlabels

What are the steps to performing cross validation on labels of data to get the accuracy of the results?

Best Answer

Repeat until the parameter estimates converges

1.Randomly divide the data into 10 subsets

2.For each subset

a. Use the remaining 9 subsets to design a model
b. Test the model with the holdout subset
c. Update the average and standard deviation of 
 the holdout test set error.
d. If std < thresh1 or std < thresh2*avg, stop.

Hope this helps.

Thank you for formally accepting my answer.

Greg

Related Solutions

MATLAB: Viewing a cross validated classification tree in the “Classification Tree Viewer”

Katie, when you crossvalidate, the output is a ClassificationPartitionedModel, which means that it contains all the cross-validated trees and will use all of them to do prediction. All the individual trees can be accessed as follows:

>> cvtree.Trained
>> view(cvtree.Trained{1},'mode','graph')
>> view(cvtree.Trained{2},'mode','graph') % etc

All the individual losses (of course based on your loss function) can be accessed as below:

>> cvtree.kfoldLoss('mode','individual')

You can view and use an individual tree if you like. Use 'kfoldPredict' to predict from the entire crossvalidated model.

MATLAB: How to best do cross-validation using fitensemble

It doesn't make any difference in the models. The only difference is that with the first method you also get a single model trained on the full dataset.

Best Answer

Related Solutions

MATLAB: Viewing a cross validated classification tree in the “Classification Tree Viewer”

MATLAB: How to best do cross-validation using fitensemble

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