I think you've misunderstood this one, Sören. This sounds like it is a
structured learning problem, where the steps are the "target" of the
learning task, and the result is the input example.
Take, for instance, the natural language processing task of dependency
parsing.
The "result" of some latent syntactic process is "He quickly ate the cat",
and the latent "steps" deriving that result are the dependency arcs:
"He" is dependent to "ate"
"quickly" is dependent to "ate"
"the" is dependent to "cat"
"cat" is dependent to "ate"
Given a sentence, we want to recover such dependency arcs.
This sort of structured learning problem is common in machine learning, but
is generally beyond the scope of scikit-learn. (The related pystruct
project by core scikit-learn dev Andreas Müller deals with this type of
task.) One of the main challenges of structured learning problems is
representing the target "steps" (i.e. the parse here) as, for example, some
previously solved class of probabilistic graphical model, such as linear
chain Conditional Random Fields, as well as representing features of the
observed instance ("result") in a way that the machine learning model can
learn to associate with the target.
HTH
Joel
On 11 January 2016 at 01:38, Sören Wacker <soeren...@gmail.com> wrote:
> Sounds like a classification problem. You can try to see the steps as
> features and use classification methods e.g. decision tree to train a
> model. But it depends on what "reconstruct the steps" and "result" means.
>
> Sören
>
>
> On 01/09/2016 06:32 AM, Dominic Laflamme wrote:
>
> First I'd like to apologize in advanced for the "noobiness" nature of my
> question...
> I'd like to get some early guidance into which path I should take to help
> me solve a problem using machine learning.
>
> The goal would be to use machine learning to "reconstruct the steps"
> needed to take in order to get to a particular "result".
> My assumption (perhaps misguided at this point) would be that I could feed
> the system with a large amount of complete "examples" (steps + result) to
> be eventually able to feed it a result in order to get a series of steps.
>
> I know my question is broad, to say the least. But I feel that knowing the
> right type of machine learning concept to apply to such a problem would
> help me started.
> Any insights on which category better suits my problem, and on whether my
> assumptions are flawed?
>
> Any and all comments greatly appreciated.
>
>
>
>
>
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