class_docstr: OneHotEncoder that can handle categorical variables.
class_docstr_tok: ["OneHotEncoder","that","can","handle","categorical","variables","."]
docstr: Convert labeled categories into one-hot encoded features.
docstr_tok: ["Convert","labeled","categories","into","one-hot","encoded","features","."]
code: def __init__(self):
      """Convert labeled categories into one-hot encoded features."""
      self._lb = LabelBinarizer()
  
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 1 to create a new CategoricalEncoder object from inherited base classes: Transformer with example: obj = CategoricalEncoder()