Class OneVsRestClassifierScikitsLearnNode

One-vs-the-rest (OvR) multiclass/multilabel strategy

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

Also known as one-vs-all, this strategy consists in fitting one classifier
per class. For each classifier, the class is fitted against all the other
classes. In addition to its computational efficiency (only `n_classes`
classifiers are needed), one advantage of this approach is its
interpretability. Since each class is represented by one and one classifier
only, it is possible to gain knowledge about the class by inspecting its
corresponding classifier. This is the most commonly used strategy for
multiclass classification and is a fair default choice.

This strategy can also be used for multilabel learning, where a classifier
is used to predict multiple labels for instance, by fitting on a 2-d matrix
in which cell [i, j] is 1 if sample i has label j and 0 otherwise.

In the multilabel learning literature, OvR is also known as the binary
relevance method.

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

**Parameters**

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

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 `n_classes` estimators
    Estimators used for predictions.

``classes_`` : array, shape = [`n_classes`]
    Class labels.
``label_binarizer_`` : LabelBinarizer object
    Object used to transform multiclass labels to binary labels and
    vice-versa.
``multilabel_`` : boolean
    Whether a OneVsRestClassifier is a multilabel classifier.

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.OneVsRestClassifier 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 : (sparse) array-like, shape = [n_samples] or [n_samples, n_classes].

Predicted multi-class targets.

Overrides: ClassifierNode.label

Fit underlying estimators.

This node has been automatically generated by wrapping the sklearn.multiclass.OneVsRestClassifier 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 : (sparse) array-like, shape = [n_samples] or [n_samples, n_classes]

Multi-class targets. An indicator matrix turns on multilabel classification.

Returns

self

Overrides: Node.stop_training