mvpa2.featsel.rfe.ClassifierError

class mvpa2.featsel.rfe.ClassifierError(clf, labels=None, train=True, **kwargs)

Compute (or return) some error of a (trained) classifier on a dataset.

Notes

Available conditional attributes:

• confusion: None
• training_stats: Proxy training_stats from underlying classifier.

(Conditional attributes enabled by default suffixed with +)

Attributes

clf
descr	Description of the object if any
labels

Methods

__call__(testdataset[, trainingdataset])	Compute the transfer error for a certain test dataset.
reset()
untrain()	Untrain the *Error which relies on the classifier

Initialization.

Parameters:

clf : Classifier

Either trained or untrained classifier

labels : list

if provided, should be a set of labels to add on top of the ones present in testdata

train : bool

unless train=False, classifier gets trained if trainingdata provided to __call__

descr : str

Description of the instance

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

Attributes

clf
descr	Description of the object if any
labels

Methods

__call__(testdataset[, trainingdataset])	Compute the transfer error for a certain test dataset.
reset()
untrain()	Untrain the *Error which relies on the classifier

clf

confusion = None

TODO Think that labels might be also symbolic thus can’t directly be indicies of the array

labels

untrain()

Untrain the *Error which relies on the classifier