In order to evaluate the performance of the data science system that you have built and check how close you are to the objective that you have in mind, you need to use a function that scores the outcome. Typically, different scoring functions are used to deal with binary classification, multilabel classification, regression, or a clustering problem. Now, let's see the most popular functions for each of these tasks and how they are used by machine learning algorithms.
Learning how to choose the right score/error measure for your data science project is really a matter of experience. We found very helpful in our practice to consult (and participate) to the data science competitions held by Kaggle (https://www.kaggle.com/), a ...