Abstract: In one embodiment, a method includes forming a diagnostic model using machine learning by ingesting trusted operational data. The method determines that a predictive data produced by an evaluated model for a period of time does not match a known trusted data for the period of time. The method analyzes whether the predictive data produced by the evaluated model for the period of time is an optimal result of the evaluated model. A determination is made that a mistake occurred when the predictive data is not the optimal result of the evaluated model. A cause of the mistake is explained using the diagnostic model. Lastly, fine-tuning the diagnostic model is performed based on learnings from a past predictive data for different periods of time when compared with past known trusted data for the different periods of time using a processor and a memory.