In order to evaluate the performance of the system 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.
When your task is to predict more than a single label (for instance, what's the weather like today? Which flower is this? What's your job?), it's called a multilabel classification. This is a very popular task, and many performance metrics exist to evaluate classifiers. Of course, you can use all these measures in the case of binary classification. Now, let's explain them with a simple real-world example:
In: from sklearn import datasets iris = datasets.load_iris() from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split...