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 a single 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.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Parameters : | alpha : array
labels_attr : str
space : str
enable_ca : None or list of str
disable_ca : None or list of str
postproc : Node instance, optional
descr : str
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