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Model Interpretability

Model interpretability refers to the ability for a human to understand and articulate the relationship between a model’s predictors and its outcome.  For linear models, including linear and logistic regression, these relationships are seen directly in the model coefficients.  For black-box models like neural nets, additional procedures must be overlaid on the model to yield some understanding of these relationships.