Neural Networks should learn how to say "I'm not sure"

If there is one application of Machine Learning that is known to be particularly useful and often successful, that is classification. Classification is the task of assigning a given entry to a single class (e.g. Usually, each entry to be processed is represented numerically as a vector of numbers, which can encode high-level features (e.g. the length of the tail, the presence of stripes or spots, etc.) or low-level ones (e.g. the value of each pixel in an image). Over the years, a lot of different classifiers have been explored by the community, the most popular ones being artificial neural networks, decision trees, support -vector machines, or other algorithms such as k-means clustering. In this article I will focus on neural networks, but the argument can be adapted to other types of classifiers.