Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
Summary In many supervised learning applications, understanding and visualizing the effects of the predictor variables on the predicted response is of paramount importance. A shortcoming of black box supervised learning models (e.g. complex trees, neural networks, boosted trees, random forests, nearest neighbours, local kernel-weighted methods and support vector regression) in …