Hi Krishna, I have no problem seeing the difference between n_labels=2 and n_labels=10. However the number of labels per sample can never exceed n_classes, so it is not really the mean number of labels per sample, but the expected value of the Poisson distribution from which the number of labels is sampled with samples > n_classes rejected.
n_labels is currently allowed to be set well above n_classes, but it stops being an interesting problem when all samples are labelled with all classes. Empirically we can see the average number of labels increase with n_labels, limited by n_classes: >>> for n_labels in range(1, 20): ... X, Y = make_multilabel_classification(n_samples=200, n_classes=5, n_labels=n_labels, return_indicator=True, random_state=0) ... avg_n_labels = Y.sum(axis=1).mean() ... print(n_labels, '%0.2f' % avg_n_labels) ... 1 0.96 2 1.95 3 2.66 4 3.12 5 3.73 6 3.87 7 4.08 8 4.09 9 4.30 10 4.32 11 4.33 12 4.43 13 4.56 14 4.53 15 4.62 16 4.67 17 4.61 18 4.62 19 4.64 On 19 August 2014 23:31, km <srikrishnamo...@gmail.com> wrote: > Hi all, > > I am using the make_multilabel_classification function to generate X and Y > The function help reads: > ... > n_labels : int, optional (default=2) > The average number of labels per instance. Number of labels follows > a Poisson distribution that never takes the value 0. > ... > > but then I donot see any change in X and Y generated if I change n_labels > argument value, for example from 2(default) or 10 > Is there a way I can check it ? > > > Regards, > Krishna > > > > ------------------------------------------------------------------------------ > > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
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