[v1n337]

Nice post. Thanks for sharing.

I have to point out though, that it's a bit dangerous to measure classifier accuracy as the percentage of correctly classified samples when you've no idea how the test data is skewed in favor of one class vs. the other (for binary classification, and can be generalized to multi-class problems too).

It's always much better to represent accuracy as the F1 score[1] or to just examine a confusion matrix of the predictions[2].

[1] https://en.wikipedia.org/wiki/F1_score [2] https://en.wikipedia.org/wiki/Confusion_matrix