Home | Trees | Indices | Help |
|
---|
|
Cross validate a classifier on datasets generated by a splitter from a source dataset.
Arbitrary performance/error values can be computed by specifying an error function (used to compute an error value for each cross-validation fold) and a combiner function that aggregates all computed error values across cross-validation folds.
|
|||
Inherited from |
|
|||
|
|||
|
|||
Inherited from Inherited from Inherited from Inherited from Inherited from |
|
|||
splits = StateVariable(enabled= False, doc= """Store the actua
|
|||
transerrors = StateVariable(enabled= False, doc= """Store copi
|
|||
confusion = StateVariable(enabled= False, doc= """Store total
|
|||
training_confusion = StateVariable(enabled= False, doc= """Sto
|
|||
splitter = property(fget= lambda self: self.__splitter)
|
|||
transerror = property(fget= lambda self: self.__transerror)
|
|||
combiner = property(fget= lambda self: self.__combiner)
|
|||
Inherited from Inherited from Inherited from Inherited from Inherited from |
|
|||
results Store state variable if it is enabled |
|
|||
Inherited from Inherited from Inherited from |
|
|
Perform cross-validation on a dataset. 'dataset' is passed to the splitter instance and serves as the source dataset to generate split for the single cross-validation folds.
|
|
splits
|
transerrors
|
confusion
|
training_confusion
|
Home | Trees | Indices | Help |
|
---|
Generated by Epydoc 3.0.1 on Sun Jun 1 19:01:48 2008 | http://epydoc.sourceforge.net |