In this article, I would like to talk about a common mistake new people approaching Machine Learning and classification algorithm often do. In particular, when we evaluate (and thus train) a classification algorithm, people tend to consider every misclassification equally important and equally bad. We are so deep into our mathematical version of the world that we forget about the consequences of classification errors in the real world.

But let’s start from the beginning. Imagine a simple binary classifier. It takes some input \( x \) and return a Boolean value telling use if \( x \) belongs to a certain class \( C \) or not. When we pass through the algorithm a number of elements, we can identify only 4 possible outcomes:

Use of machine learning is becoming ubiquitous and, even with a fancy name, it remains a tool in the statistical modeler belt. Every day, we leak billions of data from ourselves to companies ready to use it for their affair. Modeling through data get more common every day and mathematical model are the rulers of our life: they decide where we can work, if we can get a loan, how many years of jails we deserve, and more.

Anxiety is a human mind bug. This may seem a strange claim, but I cannot find a better explanation for anxiety disorders. In fact, we can see pathological anxiety as the undesired consequence of our ability to think about the future. Being scared about a life-threatening event in the near future is a valuable ability: it helps us to survive, avoid danger and, in short, make our species survive. That is one of the reason our species has been so successful in nature\[1\].