[scikit-learn] numpy integration with random forrest implementation

[Jacob Schreiber]

If what you're saying is that you have a variable length input, then most
sklearn classifiers won't work on this data. They expect a fixed feature
set. Perhaps you could try extracting a set of informative features being
fed into the classifier?

On Sat, Jan 21, 2017 at 3:18 AM, Carlton Banks <noflaco at gmail.com> wrote:

> Hi guys..
>
> I am currently working on a ASR project  in which the objective is to
> substitute part of the general ASR framework with some form of neural
> network, to see whether the tested part improves in any way.
>
> I started working with the feature extraction and tried, to make a neural
> network (NN) that could create MFCC features. I already know what the
> desired output is supposed to be, so the problem boils down to a simple
> input -  output mapping. Problem here is the my NN doesn’t seem to perform
> that well..  and i seem to get pretty large error for some reason.
>
> I therefore wanted to give random forrest a try, and see whether it could
> provide me a better result.
>
> I am currently storing my input and output in numpy.ndarrays, in which the
> input and output columns is consistent throughout all the examples, but the
> number of rows changes
> depending on length of the audio file.
>
> Is it possible with the random forrest implementation in scikit-learn to
> train a random forrest to map an input an output, given they are stored
> numpy.ndarrays?
> Or do i have do it in a different way? and if so how?
>
> kind regards
>
> Carl truz
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