An unexpected unity among methods for interpreting model predictions

Understand

Understanding why a model made a certain prediction is crucial in many data science fields.

• Interpretable predictions engender appropriate trust and provide insight into how the model may be improved.
• However, with large modern datasets the best accuracy is often achieved by complex models even experts struggle to interpret, which creates a tension between accuracy and interpretability.
• Recently, several methods have been proposed for interpreting predictions from complex models by estimating the importance of input features.