.. _`Label Binarizer`:

.. _`org.sysess.sympathy.machinelearning.label_binarizer`:

Label Binarizer
~~~~~~~~~~~~~~~

.. image:: label_binarizer.svg
   :width: 48


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

*Configuration*:


  - *pos_label*

    Value with which positive labels must be encoded.


  - *neg_label*

    Value with which negative labels must be encoded.




*Attributes*:


  - *classes_*

    Holds the label for each class.


  - *y_type_*

    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'.




*Input ports*:


*Output ports*:
    **model** : model
        Model



**pos_label** (pos_label)
    Value with which positive labels must be encoded.
**neg_label** (neg_label)
    Value with which negative labels must be encoded.

.. automodule:: node_preprocessing

.. class:: LabelBinarizer