I am not surprised that there were no apparent correlations between the raw frequency and the follower frequency. Though classification is still about 70% neutral, when looking at the same draw between the two functions, they are rarely lining up as NNN on both.

Now it is time to grab some paper and start rearranging the data and looking for clues. Could a spreadsheet do the same? Sure, but there is something about actually writing stuff down that helps connect to the data. If something looks useful during the manual process, it is most likely simple to automate, but that part is for later.

With the script, I am passing in variables, so it is worth tweaking those, and much easier since I only have to change a variable to call the functions with a new setup. The exciting find of a "scaling factor" to control how much of the standard deviation is used will be helpful in "tuning" the classifiers between the 2 functions.

This factor I am calling neutral_bandwidth, as it scales the classification thresholds by multiplying the standard deviation by a scaling factor. If st_dev = 2, then setting this variable to 0.7 effectively reduces the st_dev to 1.4. By the same token, using 1.3 as a bandwidth factor increases the same st_dev to 2.6

I found this necessary, because even though the numbers have the same chance of being drawn with both functions, they don't seem to distribute in exactly the same way.

This part may take some time...