Background: I am developing a Python Statistics Framework, not because the ones out there are bad but because it will help me learn Python and Statistics. I have taken AP Stats, and read scattered books and articles beyond. I am more than willing to read up on whatever shiny technique will solve my problem: The issue is that I don't know the name of said technique yet.
Problem: Given two or more things, each of which takes in an X value and returns the probability of that result(Within a certain fixed range, so .00 to .01, .01 to .02 would each be separate blocks catching all the x values within that range and returning .005 and .015 respectively), and a data set, quantitatively figure out which function best matches the data. Doing the reverse(taking in a probability and returning an X value) would be a bonus.
Idea: Be able to compare Logistic Regression, a Data Tree, and "If yes within the past 3 years then .8 else .01" style predictions.
Is there a sane way to do this? Thank you.