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Topic: Degree-of-freedom adjustments for classification error?
Replies: 2   Last Post: Sep 10, 2013 2:47 AM

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Greg Heath

Posts: 214
Registered: 12/13/04
Degree-of-freedom adjustments for classification error?
Posted: Sep 7, 2013 12:52 PM
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I have been using the estimation degrees of freedom expression

Ndof = Ntrneq - Nw

for mitigating the bias in the MSE estimate of nonlinear neural network
regression models when the training data is used for the estimate.

Ntrn - Number of input/target training example vector pairs

O - Dimensionality of the target/output vectors

Ntrneq - Number of scalar training equations: Ntrneq = Ntrn*O

Nw - Number of unknown weights that have to be estimated

SSEtrn - Sum-squared-error

MSEtrn - Biased mean-squared-error estimate: MSEtrn = SSEtrn/Ntrneq

MSEtrna - Adjusted mean-squared-error estimate: MSEtrna = SSEtrn/Ndof

My question is: Is there a similar adjustment for classifiers?

PctErr = 100*Ntrnerr/Ntrneq

PctErra = 100*Ntrnerr/Ndof ????



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