The nutcracker framework for ensemble interpretability

The basic principles behind ensembles (e.g.
Random Forest, AdaBoost) are simple. But we’re still
in trouble when attempting to explain the logic taken.
Where does the problem lie? The reason that ensembles
are effective is that the base estimators "work
together" and compensate each for the others’
shortcomings.
The Nutcracker Framework Given a trained ensemble
and the relevant training / test dataset, construct prediction
matrix, M, cases (rows) against predictions (columns).
Bicluster M to a given number of R x C biclusters.
Now, investigate performance per bicluster (R x C).
Identify feature importance per base estimators group (C).
Describe each of the R cases subgroups in terms of
features and values. We use Exceptional Model Mining for that task.
Performance of the ensemble against the dataset
compared to performance of base estimator groups
against subgroups of cases, adds transparency.