Class OneVsOneClassifierScikitsLearnNode

One-vs-one multiclass strategy

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

This strategy consists in fitting one classifier per class pair.
At prediction time, the class which received the most votes is selected.
Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers,
this method is usually slower than one-vs-the-rest, due to its
O(n_classes^2) complexity. However, this method may be advantageous for
algorithms such as kernel algorithms which don't scale well with
`n_samples`. This is because each individual learning problem only involves
a small subset of the data whereas, with one-vs-the-rest, the complete
dataset is used `n_classes` times.

Read more in the :ref:`User Guide <ovo_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 * (n_classes - 1) / 2` estimators
    Estimators used for predictions.

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

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Estimate the best class label for each sample in X.

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

This is implemented as argmax(decision_function(X), axis=1) which will return the label of the class with most votes by estimators predicting the outcome of a decision for each possible class pair.

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.OneVsOneClassifier 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 : array-like, shape = [n_samples]

Multi-class targets.

Returns

self

Overrides: Node.stop_training