We're a little crazy, about science!

Day #114: Classifier Update

NARXNET Classifier-Raw out

Behold! My amazing predictive power!! *insert evil laugh here*

It’s been a long and exhaustive road to get to here. As you may recall, I’ve given several updates already on my progress and I THINK I’ve finally hit success. In fact, I know I have, the problem now? Well what the neural net saw to make the predictions is a mystery to me, so I don’t know why or how it works… yet.

Neural nets are sort of like a black box. To add to the problems (sort of) this was a non-linear neural network (a closed loop NARXNET to be exact). I’ve posed what that looks like before, but if you need a reminder, we are talking about a closed loop neural network that looks like this:

NARXNET figure

In fact, that is mine. It took a lot of messing around with it to get everything to work properly, a LOT of trial and error, restarts, and adjustments to the setup. However, I managed a 98.8% accuracy, not too shabby if I do say so. 

Now, I’ve been promised accuracy like that before, so caution is required. HOWEVER, that was the accuracy I got after I fed it data it had never seen. THAT was what I wanted and I’m super happy with the result. In comparison, my “simple” SVM linear classifier gave me an accuracy of 81% with the training dataset and a 73% accuracy rate with data it had never seen. Not horrible, but not in the 90% range I wanted. Note: SVM classifiers (support vector machine classifiers) are not simple, hence the quotes around it.

Cervical - SVM Classifier - Kfold

Linear SVM classifier results with a test set (unseen dataset) after doing a K-fold cross validation.

Here’s the deal though, I probably won’t be using this for my QE. There was no point, it’s a result I cannot really explain, but it gives me a good start for future work and shows that what I’m doing works. In essence, it’s a proof of concept, not something that I will be sharing with everyone explicitly. Still, it’s pretty freaking cool and gives me something to look forward to.

Until next time, don’t stop learning!

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