Class OutputCodeClassifierScikitsLearnNode

(Error-Correcting) Output-Code multiclass strategy

This node has been automatically generated by wrapping the ``sklearn.multiclass.OutputCodeClassifier`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Output-code based strategies consist in representing each class with a
binary code (an array of 0s and 1s). At fitting time, one binary
classifier per bit in the code book is fitted.  At prediction time, the
classifiers are used to project new points in the class space and the class
closest to the points is chosen. The main advantage of these strategies is
that the number of classifiers used can be controlled by the user, either
for compressing the model (0 < code_size < 1) or for making the model more
robust to errors (code_size > 1). See the documentation for more details.

Read more in the :ref:`User Guide <ecoc>`.

**Parameters**

estimator : estimator object
    An estimator object implementing `fit` and one of `decision_function`
    or `predict_proba`.

code_size : float
    Percentage of the number of classes to be used to create the code book.
    A number between 0 and 1 will require fewer classifiers than
    one-vs-the-rest. A number greater than 1 will require more classifiers
    than one-vs-the-rest.

random_state : numpy.RandomState, optional
    The generator used to initialize the codebook. Defaults to
    numpy.random.

n_jobs : int, optional, default: 1
    The number of jobs to use for the computation. If -1 all CPUs are used.
    If 1 is given, no parallel computing code is used at all, which is
    useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are
    used. Thus for n_jobs = -2, all CPUs but one are used.

**Attributes**

``estimators_`` : list of `int(n_classes * code_size)` estimators
    Estimators used for predictions.

``classes_`` : numpy array of shape [n_classes]
    Array containing labels.

``code_book_`` : numpy array of shape [n_classes, code_size]
    Binary array containing the code of each class.

**References**


.. [1] "Solving multiclass learning problems via error-correcting output
   codes",
   Dietterich T., Bakiri G.,
   Journal of Artificial Intelligence Research 2,
   1995.

.. [2] "The error coding method and PICTs",
   James G., Hastie T.,
   Journal of Computational and Graphical statistics 7,
   1998.

.. [3] "The Elements of Statistical Learning",
   Hastie T., Tibshirani R., Friedman J., page 606 (second-edition)
   2008.

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict multi-class targets using underlying estimators.

This node has been automatically generated by wrapping the sklearn.multiclass.OutputCodeClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : (sparse) array-like, shape = [n_samples, n_features]

Data.

Returns

y : numpy array of shape [n_samples]

Predicted multi-class targets.

Overrides: ClassifierNode.label

Fit underlying estimators.

This node has been automatically generated by wrapping the sklearn.multiclass.OutputCodeClassifier class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : (sparse) array-like, shape = [n_samples, n_features]

Data.

y : numpy array of shape [n_samples]

Multi-class targets.

Returns

self

Overrides: Node.stop_training