Abstract: A nonparametric counting process to assist with defining a cumulative probability of an in-class observation occurring by a score segment. A Markov process state space model can be applied to evaluate the stochastic process of observations over the classification model score. A new definition for the recall curve may be formulated as the cumulative probability of in-class observations being classified as in-class observations, true positives. A novel hypothesis test is provided to compare the performance of black box models. Explanations attribute a likelihood of in-class observations to feature inputs used in the black box model, even when the features are time series and in order dependent models such as recurrent neural networks. Censoring is provided to use information from the time dependence of the features and unlabeled observations to derive global and local explanations.