Bayesian hypothesis testing on confusion matrices.
For multi-class classification a single accuracy value is often not a meaningful performance measure – or at least hard to interpret. This class allows for convenient Bayesian hypothesis testing of confusion matrices. It computes the likelihood of discriminibility of any partitions of classes given a confusion matrix.
The returned dataset contains at least one feature (the log likelihood of a hypothesis) and as many samples as (possible) partitions of classes. The actual partition configurations are stored in a sample attribute of nested lists. The top-level list contains discriminable groups of classes, whereas the second level lists contain groups of classes that cannot be discriminated under a given hypothesis. For example:
[[0, 1], [2], [3, 4, 5]]
This hypothesis represent the state where class 0 and 1 cannot be distinguish from each other, but both 0 and 1 together can be distinguished from class 2 and the group of 3, 4, and 5 – where classes from the later group cannot be distinguished from one another.
This algorithms is based on
Olivetti, E., Greiner, S. and Avesani, P. (2012). Testing for Information with Brain Decoding. In: Pattern Recognition in NeuroImaging (PRNI), International Workshop on.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Parameters : | alpha : array
labels_attr : str
space : str
prior_Hs : array
log : bool
postprob : bool
hypotheses : list
enable_ca : None or list of str
disable_ca : None or list of str
postproc : Node instance, optional
descr : str
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