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One of the most common problems preventing the application of predictionmodels in the real world is lack of generalization: The accuracy of models,measured in the benchmark does repeat itself on future data, e.g. in thesettings of real business. There is relatively little methods exist thatestimate the confidence of prediction models. In this paper, we propose novelmethods that, given a neural network classification model, estimate uncertaintyof particular predictions generated by this model. Furthermore, we propose amethod that, given a model and a confidence level, calculates a threshold thatseparates prediction generated by this model into two subsets, one of themmeets the given confidence level. In contrast to other methods, the proposedmethods do not require any changes on existing neural networks, because theysimply build on the output logit layer of a common neural network. Inparticular, the methods infer the confidence of a particular prediction basedon the distribution of the logit values corresponding to this prediction. Theproposed methods constitute a tool that is recommended for filteringpredictions in the process of knowledge extraction, e.g. based on webscrapping, where predictions subsets are identified that maximize the precisionon cost of the recall, which is less important due to the availability of data.The method has been tested on different tasks including relation extraction,named entity recognition and image classification to show the significantincrease of accuracy achieved.

Confidence estimation of classification based on the distribution of the neural network output layer