Scalar Prediction

Modified on Fri, 16 Dec 2022 at 06:41 PM

Description

Instantly view the predictions of single numerical values for a new selection of input parameters. Requires a trained Model for Tabular Data.


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.


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 Outputs you want to predict. You can choose multiple outputs.
  • If you have used the step Fix Parameters in this notebook, you could select a set of fixed parameters for that prediction.
  • 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 prediction(s) will be updated in a few seconds.
  • The range of the input sliders is defined by the min and max values of the inputs in the training set of the model.

Examples

In the figure below, a model was used to predict the value of Output 1 based on seven inputs. Each time an input value is modified, the output value changes accordingly. As the model used for the prediction was trained with the option to predict uncertainty, you can see a shaded range around the dial that indicates the uncertainty of the prediction. You can see that some predictions are more uncertain than others, and this can help you when making decisions.

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