Explainability refers to the ability of a machine learning model to be understood and explained in a clear and understandable way by humans. Explainability is important because many machine learning models are very complex and difficult to interpret, which can make it difficult to understand how decisions or predictions are arrived at.
Explainability is particularly important in critical applications where it is necessary to understand how decisions are made, such as fraud detection or medical decision-making. Machine learning models that are highly explainable allow subject matter experts to understand how decisions are made and explain them to others in an understandable way.
Several techniques exist to increase the explainability of machine learning models, such as data visualisation, model simplification, identification of important features, and interpretation of the decisions made by the model. In addition, explainability can also be improved by using machine learning models that are inherently more explainable, such as rule-based models and linear models.
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