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.
