Tuesday, 31 July 2012

Successful Completion of Neural Net Cross Validation Tests

In my last post I suggested that I was unsure of my coding of the cross validation test I had written so what I have done is take a new coding approach and completely rewritten the test, which I'm happy to say has been very successful. Using this newly coded implementation the out of sample accuracy of the trained neural nets is 100 %. As before, these tests were run overnight, but this time for a total of 2,400,000 separate test examples due to increased code efficiency.

The next test I'm going to code, more out of curiosity than anything else, is a concurrent cross validation test to test both my new neural net classifier algorithm and my Naive Bayesian Classifier together. I expect the NN to again obtain results similar to the above, but anticipate that the Naive Bayesian Classifier will perform quite poorly, achieving between 20 % to 30 % accuracy. I expect such low performance simply because the Naive Bayesian Classifier was developed using just 5 exemplar market type examples compared to 25 for the NN.

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