mvpa2.clfs.meta.SplitClassifier

Inheritance diagram of SplitClassifier

class mvpa2.clfs.meta.SplitClassifier(clf, partitioner=NFoldPartitioner(), 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
  • 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
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
  • 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

get_sensitivity_analyzer(*args_, **kwargs_)
partitioner

Partitioner used by SplitClassifier

splitter

Splitter used by SplitClassifier

NeuroDebian

NITRC-listed