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clfs.ridge
Module: clfs.ridge
Inheritance diagram for mvpa.clfs.ridge:
Ridge regression classifier.
-
class mvpa.clfs.ridge.RidgeReg(lm=None, **kwargs)
Bases: mvpa.clfs.base.Classifier
Ridge regression Classifier.
This ridge regression adds an intercept term so your labels do not
have to be zero-centered.
Note
Available state variables:
- feature_ids: Feature IDS which were used for the actual training.
- predicting_time+: Time (in seconds) which took classifier to predict
- predictions+: Most recent set of predictions
- trained_dataset: The dataset it has been trained on
- trained_labels+: Set of unique labels it has been trained on
- trained_nsamples+: Number of samples it has been trained on
- training_confusion: Confusion matrix of learning performance
- training_time+: Time (in seconds) which took classifier to train
- values+: Internal classifier values the most recent predictions are based on
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Classifier
Initialize a ridge regression analysis.
Parameters: |
- lm (float) – the penalty term lambda.
(Defaults to .05*nFeatures)
- regression – Either to use ‘regression’ as regression. By default any Classifier-
derived class serves as a classifier, so regression does binary
classification. (Default: False)
- enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally
to default ones
- disable_states (None or list of basestring) – Names of the state variables which should be disabled
|