The visual encoding of points in the scatter plot and hexagons in the aggregation can be changed to better comprehend a parameter space (T1) in the twodimensional space and identify critical factors (T3). For example, users can choose to encode the size of points in the scatter plot by temperature or choose a color encoding to distinguish high and low yield. The aggregation hexagons can, for ex-ample, be colored by the value of the acquisition function to see which area in the parameter space the model deems best to explore next. This functionality can also be used to understand model predictions (T4).