5. The computer program product of claim 1 , wherein said training comprises: training the classifier tool based on the training dataset and associated labels; obtaining predictions by the classifier tool with respect to at least a portion of the dataset; and training the reliability classifier tool based on the training dataset and correlation between the predicted label and the associated label with respect to the same data-point.
1. A computer program product comprising: a non-transitory computer readable medium retaining program instructions, which instructions when read by a processor, cause the processor to perform: obtaining a training dataset, the training dataset comprises a sample of data-points, wherein with respect to each data-point a label is provided; training a reliability classifier tool using the training dataset and predictions made with respect to data-points of the training dataset made by the classifier tool in response to a training session, wherein the training session comprises training the classifier tool with respect to a portion of the training dataset; obtaining a prediction of a label for a dataset made by a classifier tool, wherein the classifier tool is aimed at predicting the label based on a classification model and in view of a set of features defining the dataset; obtaining a reliability prediction of a reliability label for the prediction based on the reliability classifier tool, wherein the reliability classifier tool is aimed at predicting the reliability label based on a classification model and in view of a second set of features, wherein the second set of features comprises at least one feature that is not comprised by the set of features and at least one feature that is comprised by the set of features; and outputting, at the same stage, to a user the label prediction and the reliability label which indicates a reliability of the label prediction.