My input data consists of a series of 8 integers. Each integer is a discrete token, rather than a relative numeric value (i.e. '1' and '2' are as distinct as are '1' and '100'). The output is a single binary value indicating success or fail. For example:
fail,12,35,60,82,98,111,142,161 success,23,46,59,87,102,121,145,161 fail,13,35,65,83,100,102,122,161
I have say 500,000 of these entries.
Success or failure is determined by the combination of the eight tokens that go to make up the input. I am certain that no single token will dictate success or failure, but there may be particular tokens or combinations of tokens which are significant in determining success or failure, I don't know, but would like to know.
My question is, what kind of machine learning algorithm should I implement to answer the question of which tokens and combinations of tokens are most likely to lead to success?
In case it's relevant or useful, a few more notes on the input data:
There is a limited range of tokens (and thus integers) in each slot. So with this data input:
A is always say one of 1, 2, 3, 4 or 5. B is always one of 6, 7 or 8. C is always one of 9, 10, 11 or 12. So in the general case, possible values for A are never possible values for the other slots and there are between 2 and 12 values for each slot. No idea if that makes a different to the answer but wanted to include it for completeness.