Querying posterior distributions

The third way to use the library is to construct and query posterior distributions.

We construct the posterior distribution by calling the corresponding test class. If j48 and nbc contain scores from cross validation on a single data set, we construct the posterior by

>>> posterior = CorrelatedTTest(nbc, j48)

and then compute the probabilities and plot the histogram

>>> posterior.probs()
(0.4145119975061462, 0.5854880024938538)
>>> fig = posterior.plot(names=("nbc", "j48"))

For comparison on multiple data sets we do the same, except that nbc and j48 must contain average classification accuracies (for sign test and signed rank test) or a matrix of accuracies (for hierarchical test).

>>> posterior = SignedRankTest(nbc, j48, rope=1)
>>> posterior.probs()
(0.23014, 0.00674, 0.76312)
>>> fig = posterior.plot(names=("nbc", "j48"))

Single data set

Multiple data sets