Oh okay. But that shouldn’t be a problem, the RandomForestRegressor also supports multi-outpout regression; same expected target array shape: [n_samples, n_outputs]
Best, Sebastian > On Jan 21, 2017, at 1:27 PM, Carlton Banks <nofl...@gmail.com> wrote: > > 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 _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
