Thanks for the Info!.. How do you set it up.. There doesn’t seem a example available for regression purposes.. > Den 21. jan. 2017 kl. 19.32 skrev Sebastian Raschka <se.rasc...@gmail.com>: > > 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
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