qiskit.aqua.components.multiclass_extensions.OneAgainstRest

class OneAgainstRest

The One Against Rest multiclass extension.

For an \(n\)-class problem, the one-against-rest method constructs \(n\) SVM classifiers, with the \(i\)-th classifier separating class \(i\) from all the remaining classes, \(\forall i \in \{1, 2, \ldots, n\}\). When the \(n\) classifiers are combined to make the final decision, the classifier that generates the highest value from its decision function is selected as the winner and the corresponding class label is returned.

__init__()

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__()	Initialize self.
predict(x)	Applying multiple estimators for prediction.
set_estimator(estimator_cls[, params])	Called internally to set Estimator and parameters :type estimator_cls: Callable[[List], Estimator] :param estimator_cls: An Estimator class :type params: Optional[List] :param params: Parameters for the estimator
test(x, y)	Testing multiple estimators each for distinguishing a pair of classes.
train(x, y)	Training multiple estimators each for distinguishing a pair of classes.

predict(x)

Applying multiple estimators for prediction.

Parameters

x (numpy.ndarray) – NxD array

Returns

predicted labels, Nx1 array

Return type

numpy.ndarray

set_estimator(estimator_cls, params=None)

Called internally to set Estimator and parameters :type estimator_cls: Callable[[List], Estimator] :param estimator_cls: An Estimator class :type params: Optional[List] :param params: Parameters for the estimator

Return type

None

test(x, y)

Testing multiple estimators each for distinguishing a pair of classes.

Parameters

• x (numpy.ndarray) – input points

• y (numpy.ndarray) – input labels

Returns

accuracy

Return type

float

train(x, y)

Training multiple estimators each for distinguishing a pair of classes.

Parameters

• x (numpy.ndarray) – input points

• y (numpy.ndarray) – input labels

Raises

Exception – given all data points are assigned to the same class, the prediction would be boring