I have posted a few times here about a rich dataset that I have. Here is what it looks like. Click on each picture to enlarge.
Each of the 5296 rows represents a sequential day in a 14.5 year span of Amazon review misspelling rates during Jan 1, 2000 to Jul 1, 2014.
Across the top are the labels. In each column is a simple, stable, linear function of the right ascension (the Tropical degree) of the planet, moon, or star at midnight at the start of that day in London, UK. Retrogressions of the planets are also included.
The final column is the log of difference of the misspelling rate of the day from the 27-day SNIP baseline. (The Moon's right ascension completes its cycle every 27 and change days. That is the shortest cycle for any of the right ascensions.) The following is a graph of this column's data over time.
Note that the SNIP method comes from signal processing and tends to preserve cyclic behavior in spectra.
For today's study, the data for the first 80% of days were developed into a training group, and that of the subsequent 20% of days were isolated as a test group.
An automated machine learning algorithm from BigML.com called a DeepNet* was applied to the training set of the first 80% of days. This DeepNet was then tested or evaluated on the last 20% of days.
After less than a minute of computation at default ("1-click") settings, these ridiculously good results ensued.
Renay Oshop - teacher, searcher, researcher, immerser, rejoicer, enjoying the interstices between Twitter, Facebook, and journals.