There are numerous techniques for fitting regression models to data, ranging from classical multiple linear regression to highly sophisticated approaches such as spline-based, tree-based, rule-based, neural network, and ensemble methods. Although it is important in many applications that a regression model be interpretable, research in this area is mostly driven by prediction accuracy. It seems almost a fact that the more sophisticated an algorithm, the less interpretable its models become. In this talk, we discuss some basic problems that hinder model interpretation and propose that the most interpretable model is one that can be visualized graphically. The challenge is how to build such a model without unduly sacrificing prediction accuracy. We propose one solution and compare its prediction accuracy with other methods.