> In SBM, Quality Assurance (QA) most often requires some additional, rather laborious actions, like special verification experiments and testing. In EBM **QA is a naturally integrated part
of the modeling process**. And since the examples (used for model training) ARE the reality, this **reality IS the model** so to say, beside of some **measurable errors for the single
parameters** of the model at hand. Of course, these errors give valuable hints on the suitability of any single parameter, and additionally on the **overall modeling quality** of a given
parameter set, which offers a great help on the way to find the best parameter set for a given problem.

> An immanent **problem** of EBM is "**overfitting**", which results in a rather well-fitting model for a given set of examples, but lets the model fail, if more examples (even from the
same sources) are added. To avoid this effect, "**rotating** **training** **and** **test** **sets**" are used during model training. If there are enough examples available, this is a well-known and solid way to avoid "overfitting" and to ensure **GENERALIZATION**, to model the true inherent relationships between the parameters.
Of course, this procedure can come to some boundaries for "sparse problems", when only a rather small number of examples is available.

> Another general problem for high-dimendionsl models can show up at deployment time: Again because of the max 2-5 dimensional capabilities of human brains, a
user can not have a **measure**, **how** **reliably** **a** **model** **answer** may be at a given point in the n-dimensional problem space. In our CAEBM technology, we overcome
this problem with the aid of a special neural net, modeling the density of examples over the n-dimensional problem space. So the resulting "**reliability** **indicator**" can measure the
applicability of the model at hand for any given point in its parameter space.

> In general, CAEBM does not provide a model only for a given problem, but also **reliable measures for the model quality,** as well in terms of global
indicators as for every single parameter involved, which cannot be expected in most SBM processes. And, of course, powerful CA is a most important ingredient to make these CAEBM qualities
possible.

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