Not classifiication… but regression.. and yes both the input and output should be stored stored like that..
> Den 21. jan. 2017 kl. 19.24 skrev Sebastian Raschka <se.rasc...@gmail.com>: > > Hi, Carlton, > sounds like you are looking for multilabel classification and your target > array has the shape [n_samples, n_outputs]? If the output shape is consistent > (aka all output label arrays have 13 columns), you should be fine, otherwise, > you could use the MultiLabelBinarizer > (http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html#sklearn.preprocessing.MultiLabelBinarizer). > > Also, the RandomForestClassifier should support multillabel classification. > > Best, > Sebastian > >> On Jan 21, 2017, at 12:59 PM, Carlton Banks <nofl...@gmail.com> wrote: >> >> Most of the machine learning library i’ve tried has an option of of just >> give the dimension… >> In this case my input consist of an numpy.ndarray with shape (x,2050) and >> the output is an numpy.ndarray with shape (x,13) >> x is different for each set… >> But for each set is the number of columns consistent. >> >> Column consistency is usually enough for most library tools i’ve worked >> with… >> But is this not the case here? >>> Den 21. jan. 2017 kl. 18.42 skrev Jacob Schreiber <jmschreibe...@gmail.com>: >>> >>> I don't understand what you mean. Does each sample have a fixed number of >>> features or not? >>> >>> On Sat, Jan 21, 2017 at 9:35 AM, Carlton Banks <nofl...@gmail.com> wrote: >>> Thanks for the response! >>> >>> If you see it in 1d then yes…. it has variable length. In 2d will the >>> number of columns always be constant both for the input and output. >>> >>>> Den 21. jan. 2017 kl. 18.25 skrev Jacob Schreiber >>>> <jmschreibe...@gmail.com>: >>>> >>>> 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 <nofl...@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 >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
