sklearn.preprocessing.MultiLabelBinarizer  
class sklearn.preprocessing.MultiLabelBinarizer(*, classes=None, sparse_output=False) [source]
 
Transform between iterable of iterables and a multilabel format. Although a list of sets or tuples is a very intuitive format for multilabel data, it is unwieldy to process. This transformer converts between this intuitive format and the supported multilabel format: a (samples x classes) binary matrix indicating the presence of a class label.  Parameters 
 
classesarray-like of shape (n_classes,), default=None 

Indicates an ordering for the class labels. All entries should be unique (cannot contain duplicate classes).  
sparse_outputbool, default=False 

Set to True if output binary array is desired in CSR sparse format.    Attributes 
 
classes_ndarray of shape (n_classes,) 

A copy of the classes parameter when provided. Otherwise it corresponds to the sorted set of classes found when fitting.      See also  
OneHotEncoder


Encode categorical features using a one-hot aka one-of-K scheme.    Examples >>> from sklearn.preprocessing import MultiLabelBinarizer
>>> mlb = MultiLabelBinarizer()
>>> mlb.fit_transform([(1, 2), (3,)])
array([[1, 1, 0],
       [0, 0, 1]])
>>> mlb.classes_
array([1, 2, 3])
 >>> mlb.fit_transform([{'sci-fi', 'thriller'}, {'comedy'}])
array([[0, 1, 1],
       [1, 0, 0]])
>>> list(mlb.classes_)
['comedy', 'sci-fi', 'thriller']
 A common mistake is to pass in a list, which leads to the following issue: >>> mlb = MultiLabelBinarizer()
>>> mlb.fit(['sci-fi', 'thriller', 'comedy'])
MultiLabelBinarizer()
>>> mlb.classes_
array(['-', 'c', 'd', 'e', 'f', 'h', 'i', 'l', 'm', 'o', 'r', 's', 't',
    'y'], dtype=object)
 To correct this, the list of labels should be passed in as: >>> mlb = MultiLabelBinarizer()
>>> mlb.fit([['sci-fi', 'thriller', 'comedy']])
MultiLabelBinarizer()
>>> mlb.classes_
array(['comedy', 'sci-fi', 'thriller'], dtype=object)
 Methods  
fit(y) Fit the label sets binarizer, storing classes_.  
fit_transform(y) Fit the label sets binarizer and transform the given label sets.  
get_params([deep]) Get parameters for this estimator.  
inverse_transform(yt) Transform the given indicator matrix into label sets.  
set_params(**params) Set the parameters of this estimator.  
transform(y) Transform the given label sets.    
fit(y) [source]
 
Fit the label sets binarizer, storing classes_.  Parameters 
 
yiterable of iterables 

A set of labels (any orderable and hashable object) for each sample. If the classes parameter is set, y will not be iterated.    Returns 
 
selfreturns this MultiLabelBinarizer instance 
   
  
fit_transform(y) [source]
 
Fit the label sets binarizer and transform the given label sets.  Parameters 
 
yiterable of iterables 

A set of labels (any orderable and hashable object) for each sample. If the classes parameter is set, y will not be iterated.    Returns 
 
y_indicator{ndarray, sparse matrix} of shape (n_samples, n_classes) 

A matrix such that y_indicator[i, j] = 1 i.f.f. classes_[j] is in y[i], and 0 otherwise. Sparse matrix will be of CSR format.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
inverse_transform(yt) [source]
 
Transform the given indicator matrix into label sets.  Parameters 
 
yt{ndarray, sparse matrix} of shape (n_samples, n_classes) 

A matrix containing only 1s ands 0s.    Returns 
 
ylist of tuples 

The set of labels for each sample such that y[i] consists of classes_[j] for each yt[i, j] == 1.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(y) [source]
 
Transform the given label sets.  Parameters 
 
yiterable of iterables 

A set of labels (any orderable and hashable object) for each sample. If the classes parameter is set, y will not be iterated.    Returns 
 
y_indicatorarray or CSR matrix, shape (n_samples, n_classes) 

A matrix such that y_indicator[i, j] = 1 iff classes_[j] is in y[i], and 0 otherwise.