Machine Learning 101: ML Algorithms with K-Fold Cross Validation

Good grief, how many times are you going to submit this? https://news.ycombinator.com/submitted?id=miggyzerp21

"Naïve Bayes can only represent non-negative frequency counts of features; therefore it was not a candidate as accelerometer data has negative values. However, this could be mediated by simply scaling all the data to ensure positive values (i.e. multiplying all values times 100)."

I think there are two serious flaws here.

- Bayesian frequency counts aren't measured values - they're counts of measured values...

- Multiplying a dataset of positive and negative numbers isn't going to make it strictly positive (unless you multiply by 0). You'd have to add the minimum value to all values.

This lead me to look for the author, but sadly the author is some anonymous 'GuestBlogger' ...