Supervised machine learning constitutes the set of techniques that work towards building a model that approximate a function. The function takes a set of input variables, which are alternatively called independent variables, and tries to map the input variables to the output variable, alternatively called the dependent variable or the label.
Given that we know the label (or the value) we are trying to predict, for a set of input variables, the technique becomes a supervised learning problem.
In a similar manner, in an unsupervised learning problem, we do not have the output variable that we have to predict. However, in unsupervised learning, we try to group the data points so that they form logical groups.
A distinction between supervised and unsupervised learning at a high level can be obtained as shown in the following diagram:
In...
