class_docstr: Binarize labels in a one-vs-all fashion
  
  Several regression and binary classification algorithms are
  available in the scikit. A simple way to extend these algorithms
  to the multi-class classification case is to use the so-called
  one-vs-all scheme.
  
  At learning time, this simply consists in learning one regressor
  or binary classifier per class. In doing so, one needs to convert
  multi-class labels to binary labels (belong or does not belong
  to the class). LabelBinarizer makes this process easy with the
  transform method.
  
  At prediction time, one assigns the class for which the corresponding
  model gave the greatest confidence. LabelBinarizer makes this easy
  with the inverse_transform method.
  
  Read more in the :ref:`User Guide <preprocessing_targets>`.
  
  Parameters
  ----------
  
  neg_label : int (default: 0)
      Value with which negative labels must be encoded.
  
  pos_label : int (default: 1)
      Value with which positive labels must be encoded.
  
  sparse_output : boolean (default: False)
      True if the returned array from transform is desired to be in sparse
      CSR format.
  
  Attributes
  ----------
  
  classes_ : array of shape [n_class]
      Holds the label for each class.
  
  y_type_ : str,
      Represents the type of the target data as evaluated by
      utils.multiclass.type_of_target. Possible type are 'continuous',
      'continuous-multioutput', 'binary', 'multiclass',
      'multiclass-multioutput', 'multilabel-indicator', and 'unknown'.
  
  sparse_input_ : boolean,
      True if the input data to transform is given as a sparse matrix, False
      otherwise.
  
  Examples
  --------
  >>> from sklearn import preprocessing
  >>> lb = preprocessing.LabelBinarizer()
  >>> lb.fit([1, 2, 6, 4, 2])
  LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)
  >>> lb.classes_
  array([1, 2, 4, 6])
  >>> lb.transform([1, 6])
  array([[1, 0, 0, 0],
         [0, 0, 0, 1]])
  
  Binary targets transform to a column vector
  
  >>> lb = preprocessing.LabelBinarizer()
  >>> lb.fit_transform(['yes', 'no', 'no', 'yes'])
  array([[1],
         [0],
         [0],
         [1]])
  
  Passing a 2D matrix for multilabel classification
  
  >>> import numpy as np
  >>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]]))
  LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)
  >>> lb.classes_
  array([0, 1, 2])
  >>> lb.transform([0, 1, 2, 1])
  array([[1, 0, 0],
         [0, 1, 0],
         [0, 0, 1],
         [0, 1, 0]])
  
  See also
  --------
  label_binarize : function to perform the transform operation of
      LabelBinarizer with fixed classes.
  sklearn.preprocessing.OneHotEncoder : encode categorical integer features
      using a one-hot aka one-of-K scheme.
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docstr: Transform multi-class labels to binary labels
  
  The output of transform is sometimes referred to by some authors as the
  1-of-K coding scheme.
  
  Parameters
  ----------
  y : numpy array or sparse matrix of shape (n_samples,) or
      (n_samples, n_classes) Target values. The 2-d matrix should only
      contain 0 and 1, represents multilabel classification. Sparse
      matrix can be CSR, CSC, COO, DOK, or LIL.
  
  Returns
  -------
  Y : numpy array or CSR matrix of shape [n_samples, n_classes]
      Shape will be [n_samples, 1] for binary problems.
code: def transform(self, y):
      """Transform multi-class labels to binary labels
  
      The output of transform is sometimes referred to by some authors as the
      1-of-K coding scheme.
  
      Parameters
      ----------
      y : numpy array or sparse matrix of shape (n_samples,) or
          (n_samples, n_classes) Target values. The 2-d matrix should only
          contain 0 and 1, represents multilabel classification. Sparse
          matrix can be CSR, CSC, COO, DOK, or LIL.
  
      Returns
      -------
      Y : numpy array or CSR matrix of shape [n_samples, n_classes]
          Shape will be [n_samples, 1] for binary problems.
      """
      check_is_fitted(self, "classes_")
  
      y_is_multilabel = type_of_target(y).startswith("multilabel")
      if y_is_multilabel and not self.y_type_.startswith("multilabel"):
          raise ValueError(
              "The object was not fitted with multilabel" " input."
          )
  
      return label_binarize(
          y,
          self.classes_,
          pos_label=self.pos_label,
          neg_label=self.neg_label,
          sparse_output=self.sparse_output,
      )
  
imports: from collections import defaultdict
  import itertools
  import array
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, TransformerMixin
  from ..utils.fixes import np_version
  from ..utils.fixes import sparse_min_max
  from ..utils.fixes import astype
  from ..utils.fixes import in1d
  from ..utils import column_or_1d
  from ..utils.validation import check_array
  from ..utils.validation import check_is_fitted
  from ..utils.validation import _num_samples
  from ..utils.multiclass import unique_labels
  from ..utils.multiclass import type_of_target
  from ..externals import six
label_desc: Use image node_id 3 for calling the LabelBinarizer obj's underlying member method code with example usage: obj.transform(y) and returns: label_binarize