Model Interpretability

Model interpretability refers to the extent to which the internal workings of a machine learning model can be understood by humans. This is crucial for applications where understanding decision-making is essential, such as in healthcare or legal contexts. Techniques to improve interpretability range from simpler models that are inherently interpretable to advanced methods that provide insights into complex models.