Surface Prediction

Description

Uses a trained model to predict and plot the response surface of an output against two of the inputs, while varying the other inputs with sliders.

Application

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 (e.g. 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.

How to use

You need a trained Model for Tabular Data to be able to use this step.

• Choose the Model you want to use to make the prediction.
• Choose the Output you want to predict. Unlike for the scalar prediction, you can only choose one output.
• If you have used the step Fix Parameters in this notebook, you could select a set of fixed parameters for that prediction.
• The range of the input sliders and of the x-axis and y-axis is defined by the min and max values of the inputs in the training set of the model.
• You can add a target for the z-axis, and points reaching that target will be highlighted on the surface.
• Click Apply.
• You can then change the values of the inputs (either by changing the sliders in the step, or in a relevant step Fix Parameters), and the curve will be updated in a few seconds.

Examples

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.