Interpretability

Interpretability refers to the ability to explain or understand the reasoning behind a decision made by a machine learning model. It is an important aspect of machine learning, especially in fields where the decisions made by the model can have significant consequences, such as healthcare or finance. Interpretability can be achieved through various methods, such as feature importance analysis, visualization of decision boundaries, or generating explanations for individual predictions. The goal of interpretability is to increase transparency and trust in machine learning models, as well as to identify and correct biases or errors in the model's decision-making process.
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