In this paper, we propose a new visualization approach based on a Sen- sitivity Analysis (SA) to extract human understandable knowledge from su- pervised learning black box data mining models, such as Neural Networks (NN), Support Vector Machines (SVM) and ensembles, including Random Forests (RF). Five SA methods (three of which are purely new) and four mea- sures of input importance (one novel) are presented. Also, the SA approach is adapted to handle discrete variables and to aggregate multiple sensitivity responses. Moreover, several visualizations for the SA results are introduced, such as input pair importance color matrix and variable effect characteristic surface. A wide range of experiments was performed in order to test the SA methods and measures by fitting four well-known models (NN, SVM, RF and decision trees) to synthetic datasets (five regression and five classification tasks). In addition, the visualization capabilities of the SA are demonstrated using four real-world datasets (e.g., bank direct marketing and white wine quality).