One of the main applications of machine learning models is their speed at making new predictions. Once a model is trained on historical data, it can be used to make prediction (for example, performance, strength, cost, …) instantaneously, compared to running the simulations and tests normally required to obtain these predictions. Plotting a surface rather than predicting a simple scalar prediction enables to get more insight and see how the output will vary against 2 input parameters.
In the figure below, a model was used to predict the value of Output 1 based on Input 1 and Input 2. The other inputs will be changeable in sliders below the graph. Each time an input value is modified in one of these sliders, the whole surface will be recalculated.
