Why have these planning options been suggested?
Within this section, the best explainable visualisations to justify suggested plans are displayed. Specifically, this section will allow you to highlight how the model selected certain planning options and what criteria it followed to make those decisions.
<span data-metadata=""><span data-buffer="">Decision Tree
Description
Example from XMANAI
A decision tree is a tree-like model that breaks down a complex decision-making process into a series of simpler decisions. The tree structure consists of nodes representing decisions or test points, branches representing possible outcomes, and leaves representing the final decision or classification. They are particularly effective for modeling decision processes with multiple possible outcomes and are valued for their interpretability, as they provide a transparent representation of decision logic.
A decision tree global surrogate has been used to offer a more transparent representation of
the decision-making process for the models used to predict point measurement error for a spherical surface. The decision-tree offer an intuitive
understanding of the model’s operation, making clear how the lowest measurement error have been predicted.
