In order to evaluate the performance of the data science system that you have built and check how close you are to the goal 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 it very helpful to consult (and participate in) the data science competitions held by Kaggle (kaggle.com), a company devoted to organizing data challenges between data scientists from all over the world. By observing the various challenges and what score or error measure...
