Finally, for showing uncertain data (C4) in the hexagonal bins, we make use of bi-variate color mapping and VSUP (value-suppressing uncertainty pallets ). Bivariate color mapping is a technique that combines two principles of color encoding to represent two variables simultaneously (i.e., one variable is mapped as a variation of hue, and the second variable is mapped as varying saturation, resulting in a 2-d matrix of a mix of hues and saturation). As an alternative to the standard bi-variate color mapping, we chose to also implement VSUP, which is particularly useful if data contains uncertainty. A VSUP color map is represented in the form of a wedge, as shown in Figure . The layers of the wedge represent the uncertainty of the data (i.e., the further to the outside, the lower the uncertainty). The rationale behind this design choice is that values that are uncertain do not have to be as distinguishable from each other as those values with high certainty. For example, a model predicts the yield for every parameter setting; the variance/uncertainty depends on the data already available in a specific region of the parameter space. If the variance of the model's prediction is high, this indicates that this part of the parameter space is not yet explored well and too few data points are available to model this well. Therefore, the actual predicted value is less important. The advantage that comes with VSUP is that values with higher certainty are represented with a higher number of distinct hues, and values with lower certainty have fewer bins. This helps users to utilize and understand model predictions (T4).