mvpa2.clfs.warehouse.RegressionAsClassifier¶
- class mvpa2.clfs.warehouse.RegressionAsClassifier(clf, centroids=None, distance_measure=None, **kwargs)¶
Allows to use arbitrary regression for classification.
Possible usecases:
- Binary Classification
- Any regression could easily be extended for binary classification. For instance using labels -1 and +1, regression results are quantized into labels depending on their signs
- Multiclass Classification
- Although most of the time classes are not ordered and do not have a corresponding distance matrix among them it might often be the case that there is a hypothesis that classes could be well separated in a projection to single dimension (non-linear manifold, or just linear projection). For such use regression might provide necessary means of classification
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- distances: Distances obtained during prediction
- 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_results: Computed results before invoking postproc. Stored only if postproc is not None.
- 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 +)
Parameters : clf : Classifier XXX Should become learner
Regression to be used as a classifier. Although it would accept any Learner, only providing regressions would make sense.
centroids : None or dict of (float or iterable)
Hypothesis or prior information on location/distance of centroids for each category, provide them. If None – during training it will choose equidistant points starting from 0.0. If dict – keys should be a superset of labels of dataset obtained during training and each value should be numeric value or iterable if centroids are multidimensional and regression can do multidimensional regression.
distance_measure : function or None
What distance measure to use to find closest class label from continuous estimates provided by regression. If None, will use Cartesian distance.
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
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
