mvpa.clfs.plr

Penalized logistic regression classifier.

The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.clfs.plr (for developers).

PLR

class mvpa.clfs.plr.PLR(lm=1, criterion=1, reduced=False, maxiter=20, **kwargs)

Bases: mvpa.clfs.base.Classifier

Penalized logistic regression Classifier.

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
• 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 penalized logistic regression analysis

Parameters:

• lm (int) – the penalty term lambda.
• criterion (int) – the criterion applied to judge convergence.
• reduced (Bool) – if not False, the rank of the data is reduced before performing the calculations. In that case, reduce is taken as the fraction of the first singular value, at which a dimension is not considered significant anymore. A reasonable criterion is reduced=0.01
• maxiter (int) – maximum number of iterations. If no convergence occurs after this number of iterations, an exception is raised.
• 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

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the PLR documentation.

Full API documentation of PLR in module mvpa.clfs.plr.