Machine learning algorithms work with data. They create associations, find out relationships, discover patterns, generate new samples, and more, working with well-defined datasets. Unfortunately, sometimes the assumptions or the conditions imposed on them are not clear, and a lengthy training process can result in a complete validation failure. Even if this condition is stronger in deep learning contexts, we can think of a model as a gray box (some transparency is guaranteed by the simplicity of many common algorithms), where a vectorial input 
is transformed into a vectorial output :
Schema of a generic model parameterized ...
