Drexel dragonThe Math ForumDonate to the Math Forum



Search All of the Math Forum:

Views expressed in these public forums are not endorsed by Drexel University or The Math Forum.


Math Forum » Discussions » sci.math.* » sci.stat.math.independent

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

Advanced Search

Back to Topic List Back to Topic List Jump to Tree View Jump to Tree View   Messages: [ Previous | Next ]
Greg Heath

Posts: 214
Registered: 12/13/04
Degree-of-freedom adjustments for classification error?
Posted: Sep 7, 2013 12:52 PM
  Click to see the message monospaced in plain text Plain Text   Click to reply to this topic Reply

http://en.wikipedia.org/wiki/Adjusted_R-squared

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 ????

TIA,

Greg




Point your RSS reader here for a feed of the latest messages in this topic.

[Privacy Policy] [Terms of Use]

© Drexel University 1994-2014. All Rights Reserved.
The Math Forum is a research and educational enterprise of the Drexel University School of Education.