class_docstr: LabelEncoder that can handle categorical variables.
class_docstr_tok: ["LabelEncoder","that","can","handle","categorical","variables","."]
docstr: Transform an array of categories to a one-hot encoded
  representation.
  
  Parameters
  ----------
  X : array-like, shape=(n_samples,)
      List of categories.
  
  Returns
  -------
  Xt : array-like, shape=(n_samples, n_categories)
      The integer categories.
docstr_tok: ["Transform","an","array","of","categories","to","a","one-hot","encoded","representation",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape=","(","n_samples",",",")","List","of","categories",".","Returns","--","--","--","-","Xt",":","array-like",",","shape=","(","n_samples",",","n_categories",")","The","integer","categories","."]
code: def transform(self, X):
      """Transform an array of categories to a one-hot encoded
      representation.
  
      Parameters
      ----------
      X : array-like, shape=(n_samples,)
          List of categories.
  
      Returns
      -------
      Xt : array-like, shape=(n_samples, n_categories)
          The integer categories.
      """
      X = np.asarray(X)
      return [self.mapping_[v] for v in X]
  
imports: from __future__ import division
  import numpy
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.utils import column_or_1d
label_desc: Use image node_id 3 for calling the LabelEncoder obj's underlying member method code with example usage: obj.transform(X) and returns: unknown