  We present PREVIS, a visual analytics tool, enhancing machine learning
performance analysis in engineering applications. The presented toolchain
allows for a direct comparison of regression models. In addition, we provide a
methodology to visualize the impact of regression errors on the underlying
field of interest in the original domain, the part geometry, via exploiting
standard interpolation methods. Further, we allow a real-time preview of
user-driven parameter changes in the displacement field via visual
interpolation. This allows for fast and accountable online change management.
We demonstrate the effectiveness with an ex-ante optimization of an automotive
engine hood.
