 sklearn.datasets.make_multilabel_classification   sklearn.datasets.make_multilabel_classification(n_samples=100, n_features=20, *, n_classes=5, n_labels=2, length=50, allow_unlabeled=True, sparse=False, return_indicator='dense', return_distributions=False, random_state=None)[source]
 
Generate a random multilabel classification problem.  For each sample, the generative process is:

 pick the number of labels: n ~ Poisson(n_labels) n times, choose a class c: c ~ Multinomial(theta) pick the document length: k ~ Poisson(length) k times, choose a word: w ~ Multinomial(theta_c)    In the above process, rejection sampling is used to make sure that n is never zero or more than n_classes, and that the document length is never zero. Likewise, we reject classes which have already been chosen. Read more in the User Guide.  Parameters:
 
 
n_samplesint, default=100


The number of samples.  
n_featuresint, default=20


The total number of features.  
n_classesint, default=5


The number of classes of the classification problem.  
n_labelsint, default=2


The average number of labels per instance. More precisely, the number of labels per sample is drawn from a Poisson distribution with n_labels as its expected value, but samples are bounded (using rejection sampling) by n_classes, and must be nonzero if allow_unlabeled is False.  
lengthint, default=50


The sum of the features (number of words if documents) is drawn from a Poisson distribution with this expected value.  
allow_unlabeledbool, default=True


If True, some instances might not belong to any class.  
sparsebool, default=False


If True, return a sparse feature matrix.  New in version 0.17: parameter to allow sparse output.   
return_indicator{‘dense’, ‘sparse’} or False, default=’dense’


If 'dense' return Y in the dense binary indicator format. If 'sparse' return Y in the sparse binary indicator format. False returns a list of lists of labels.  
return_distributionsbool, default=False


If True, return the prior class probability and conditional probabilities of features given classes, from which the data was drawn.  
random_stateint, RandomState instance or None, default=None


Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.    Returns:
 
 
Xndarray of shape (n_samples, n_features)


The generated samples.  
Y{ndarray, sparse matrix} of shape (n_samples, n_classes)


The label sets. Sparse matrix should be of CSR format.  
p_cndarray of shape (n_classes,)


The probability of each class being drawn. Only returned if return_distributions=True.  
p_w_cndarray of shape (n_features, n_classes)


The probability of each feature being drawn given each class. Only returned if return_distributions=True.     
  Examples using sklearn.datasets.make_multilabel_classification
 

 Plot randomly generated multilabel dataset 

 Multilabel classification 

 

    
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