It should be simply tf = RandomForestRegressor(<some params>) rf.fit(X_train, y_train) rf.predict(X_validation) ...
Maybe also check out this documentation example here: http://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html > On Jan 21, 2017, at 1:36 PM, Carlton Banks <nofl...@gmail.com> wrote: > > 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 > > _______________________________________________ > 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
