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 <[email protected]> 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 > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn >
_______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
