Revisiting Older Experiments

Posted: September 3, 2019 at 5:46 pm

After those recent strong results with the changed code, I’m revisiting older experiments to see if the they were in fact showing promise; I’m figuring out whether it was previous features, or the previous validation method that lead to that 70% accuracy ceiling.

The 24 colour histogram feature results do not improve upon the 24 hist + 31 non-colour parameter results. I did learn a few things in the process, including that the stochastic splits change the measured accuracy of the best selected model. From this point I’ll be reporting the mean of accuracy and confusion matrices of 5 runs using different random splits of validation and test data. I also re-ran the evaluation code on the previous experiment with 24+31 features in case the good results were a fluke. Following are the results.

31 + 24 Features

Mean Accuracy:

Training78.5%
Validation79.5%
Testing77.5%

Mean of Confusion Matrices

375.0 bad predicted to be bad
106.4 bad predicted to be good
112.8 good predicted to be bad
381.8 good predicted to be good

24 Hist Features

Mean Accuracy:

Training75.9%
Validation76.1%
Testing75.3%

Mean of Confusion Matrices

531.8 bad predicted to be bad.
194.6 bad predicted to be good
155.2 good predicted to be bad.
579.4 good predicted to be good.

So the results are that the 31 + 24 features have performed much better than 24 colour hist features alone. I’m rerunning the initial and variance feature experiments using the new validation method.