Feature Importance Measure for Non-linear Learning Algorithms

Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. Unfortunately, most methods do not come with out of the box straight forward interpretation. Even linear prediction functions s(x) = ∑ j βjxj are not straight forward to explain if features β exhibit complex correlation structure.