While machine learning algorithms can produce valuable results, evaluating the performance of the resulting exercise can be a bit confusing – to the layman, that is. Often the recipient of the results will hear about Receiver Operator Characteristic curves or a KS statistic or a means squared error, or confusion matrices or some other foreign associated term. Frequently, managers and other users of predictive algorithms are not fluent in these concepts, and they might find themselves as confused as a confusion matrix might be to them. This session will focus on practical measures of performance evaluation designed for the non-quantitative executive, as well as the data scientist who needs to present the results of his efforts to less analytically inclined audiences. The end results are a simple scorecard measuring the ML algorithm performance.