Neural Net - Baseline classification above chance?
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Hello all,
I very recently started using the neural networks toolbox, and I have a basic question about the classification errors. I'm trying to sort my data into one of two categories, so baseline classification should be 50% errors. I am performing classification to predict one of 2 stimulus features at a bunch of time points. Importantly, the time points before the stimulus onset can NOT be predictive of the stimulus feature.
However, my average baseline classification errors have been only 30% in the pre-trial period ("confusion" function output "c" = .30).
1. Does this mean I should say my "empirical baseline" is 70% correct classification? 2. How could this be the case? 3. Are there better functions in the toolbox to prevent inflated estimates of classification accuracy? 4. Am I simply misunderstanding what the confusion error variable "c" means. I was undert he impression that 1-c = % correct classification (classifier accuracy).
I would appreciate any help. I realize my questions are lengthy, so I would just appreciate it if you could point me in the direction of some reading materials /resources so I can educate myself.
Thank you!
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Accepted Answer
Greg Heath
on 15 Aug 2014
Changing Notation: If there are c classes with size Ni (i=1:c), N = sum(i=1,c){Ni}, then the a priori probabilities are Pi = Ni/N ;
With no other information "prior" to making calculations and/or measurements, the Naïve Bayes Classifier will assign all inputs to the class with the maximum prior probability. The corresponding per cent classifier accuracy is 100*max(Pi).
If the classes are the same size, the percent classification accuracy is 100*(1/c) (50% for c=2, 33.3% for c=3, etc)..
Hope this helps.
Thank you for formally accepting my answer
Greg
P.S. I probably have posted details in comp.ai.neural-nets and comp.soft-sys.matlab (and maybe even ANSWERS). Try searching on
greg prior (or priors)
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