supervised learning as we mentioned earliersupervised machine learning is one of the most commonly used and successful types of machine learning in this we will describe supervised learning in more detail and explain several popular supervised learning algorithms we already saw an application of supervised machine learning in classifying iris flowers into several species using physical measurements of the flowers remember that supervised learning is used whenever we want to predict certain outcome from given inputand we have examples of input/output pairs we build machine learning model from these input/output pairswhich comprise our training set our goal is to make accurate predictions for newnever-before-seen data supervised learning often requires human effort to build the training setbut afterward automates and often speeds up an otherwise laborious or infeasible task classification and regression there are two major types of supervised machine learning problemscalled classification and regression in classificationthe goal is to predict class labelwhich is choice from predefined list of possibilities in we used the example of classifying irises into one of three possible species classification is sometimes separated into binary classificationwhich is the special case of distinguishing between exactly two classesand multiclass classificationwhich is classification between more than two classes you can think of binary classification as trying to answer yes/no question classifying emails as either spam or not spam is an example of binary classification problem in this binary classification taskthe yes/no question being asked would be "is this email spam?