Supervised learning deals with training algorithms with labeled data, inputs for which the outcome or target variables are known. It then predicts the outcome/target with the trained model for unseen future data. For example, historical e-mail data will have individual e-mails marked as ham or spam; this data is then used for training a model that can predict future e-mails as ham or spam. Supervised learning problems can be broadly divided into two major areas; classification and regression. Classification deals with predicting categorical variables or classes; for example, whether an e-mail is ham or spam or whether a customer is going to renew a subscription or not in a post-paid telecom subscription. This target variable is discrete and has a predefined set of values.
Regression deals with a target variable, which is continuous. For example, when we need to predict house prices, the target variable price is continuous and doesn't have a predefined set of values. In...
