Regression methods
Regression methods are a type of supervised learning. If the response variable is quantitative/continuous (takes on numeric values such as age, salary, height, and so on), then the problem can be called a regression problem regardless of the explanatory variables' type. There are various kinds of modeling techniques to address the regression problems. In this section, our focus will be on linear regression techniques and some different variations of it.
Regression methods can be used to predict any real valued outcomes. Following are a few examples:
Predict the salary of an employee based on his educational level, location, type of job, and so on
Predict stock prices
Predict buying potential of a customer
Predict the time a machine would take before failing
Linear regression
Further to what we discussed in the previous section Parametric methods, after the assumption of linearity is made for
(X), we need the training data to fit a model that would describe the relation between...
