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The model with the best classification error is selected from the training set and the prediction error of that model is estimated using the testing set.
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When different errors in classification associate with different costs for the user, the probability of classification error is not the best criterion for classification.
Here between classification error and feature subset size, reducing classification error is our major concern, so γ is set to 1 = (2 * 10).
In both partial sets the classification error is around 11-12 per cent, whereas in the ALL set the classification error drops significantly to 2percentnt.
Voxel classification error was also assessed.
Hence, the classification error was low.
The out of bag error classification error was 4.6%.
The set with the lowest classification error was chosen.
The mean classification error was calculated using 10 by 10 cross-validation.
Then the classification error was measured on the test set.
Classification errors were averaged over 100 runs of this protocol.
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