Provided is a method and processing unit for computer-implemented analysis of a classification model which is adapted to map, as a prediction, a number of input instances, each of them having a number n of features, into a number of probabilities of output classes, as a classification decision, according to a predetermined function, and which is adapted to determine a relevance value for each feature resulting in a saliency map. The disclosure includes the step of identifying an effect of each feature on the prediction of the instance by determining, for each feature, a relevance information representing a contextual information for all features of the instance omitting the considered feature. Then, the relevance value for each feature is determined. Finally, the plurality of relevance values for the features of the instance is evaluated to identify the effect of each feature on the prediction of the instance.