Symbolic Computation
Explainability refers to the degree to which an algorithm or model can be understood by humans, particularly in terms of its decisions and predictions. It is crucial in machine learning, as it allows users to grasp how models operate, which enhances trust, accountability, and the ability to improve systems. In the context of symbolic computation, explainability aids in bridging the gap between complex mathematical models and human interpretation.
congrats on reading the definition of Explainability. now let's actually learn it.