mvpa.clfs.meta.SplitClassifier¶
- class mvpa.clfs.meta.SplitClassifier(clf, partitioner=NFoldPartitioner(space='partitions'), splitter=Splitter(space='partitions'), **kwargs)¶
BoostedClassifier to work on splits of the data
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- estimates+: Internal classifier estimates the most recent predictions are based on
- harvested: Store specified attributes of classifiers at each split
- predicting_time+: Time (in seconds) which took classifier to predict
- predictions+: Most recent set of predictions
- raw_estimates: Estimates obtained from each classifier
- raw_predictions: Predictions obtained from each classifier
- splits: Store the actual splits of the data. Can be memory expensive
- stats: Resultant confusion whenever classifier trained on 1 part and tested on 2nd part of each split
- trained_dataset: The dataset it has been trained on
- trained_nsamples+: Number of samples it has been trained on
- trained_targets+: Set of unique targets it has been trained on
- training_stats: Confusion matrix of learning performance
- training_time+: Time (in seconds) it took to train the learner
(Conditional attributes enabled by default suffixed with +)
Initialize the instance
Parameters : clf : Classifier
classifier based on which multiple classifiers are created for multiclass
splitter : Splitter
Splitter to use to split the dataset prior training
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
clfs : list of Classifier
list of classifier instances to use
combiner : PredictionsCombiner
callable which takes care about combining multiple results into a single one (e.g. maximal vote for classification, MeanPrediction for regression))
propagate_ca : bool
either to propagate enabled ca into slave classifiers. It is in effect only when slaves get assigned - so if state is enabled not during construction, it would not necessarily propagate into slaves
auto_train : bool
Flag whether the learner will automatically train itself on the input dataset when called untrained.
force_train : bool
Flag whether the learner will enforce training on the input dataset upon every call.
space: str, optional :
Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.
postproc : Node instance, optional
Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.
descr : str
Description of the instance
harvest_attribs : list of (str or dict)
What attributes of call to store and return within harvested conditional attribute. If an item is a dictionary, following keys are used [‘name’, ‘copy’].
copy_attribs : None or str, optional
Default copying. If None – no copying, ‘copy’ - shallow copying, ‘deepcopy’ – deepcopying.
- get_sensitivity_analyzer(*args_, **kwargs_)¶
- partitioner¶
Partitioner used by SplitClassifier
- splitter¶
Splitter used by SplitClassifier
