When data scientists wish to train new models, they have a general idea of what their flow will look like. Assuming that the problem they need to solve is well defined, they need to explore the data, define labels, visualize, train, evaluate, tune and test. The most time consuming and often tedious part is data preprocessing and preparation. Should you not fully invest in this stage, you may still get a decent model – but is that good enough? What if I told you that by adding a small step, you can improve your model results and achieve greatness?

In this talk, I will present a problem that is often being ignored – identical feature vectors with different labels. We will discuss why this happens and how you can solve it in different ways in all possible domains. By the end of this discussion, you will wonder how you ever preprocessed without this phase.