# Physical interpretation of contribution of a feature in regression

I have built a random forest regression model for credit underwriting. But the business doesn't appreciate a black-box approach. So, using treeinterpreter, I have found the path taken for a prediction. But is there a physical interpretation of the contribution values. I understand that these values mean that, taking that path, the output changed by some value(positive/negative). But is there any intuitive explanation to these values that is feature-specific? Something that will make our decision tilt towards features with positive contribution values.