What is the difference between Regression and Classification?

Regression

Regression defines a type of supervised machine learning approaches that can be used to forecast any continuous-valued attribute. Regression provides some business organization to explore the target variable and predictor variable associations. It is an essential tool to explore the data that can be used for monetary forecasting and time series modeling.

Data can be smoothed by fitting the data to a function, such as with regression. Linear regression includes discovering the “best” line to fit two attributes (or variables) therefore that one attribute can be used to predict the other. Several linear regression is an advancement of linear regression, where higher than two attributes are included and the data are fit to a multidimensional space.

In linear regression, the data are modeled to fit a straight line. For example, a random variable, y (called response variable), can be modeled as a linear function of another random variable, x (called a predictor variable), with the equation y = wx+b, where the variance of y is considered to be constant.

Regression issues manage with the computation of an output value placed on input values. When used for classification, the input values are values from the database and the output values represent the classes. Regression can be used to explore classification problems, but it can be used for multiple applications such as forecasting. The elementary structure of regression is simple linear regression that contains only one predictor and a prediction.

Classification

Classification is the procedure of discovering a model that represents and distinguishes data classes or concepts, for the objective of being able to use the model to predict the class of objects whose class label is anonymous. The derived model is based on the analysis of a group of training records (i.e., data objects whose class label is familiar).

Each tuple is treated to belong to a predefined class, as determined by one of the attributes, called the class label attribute. In the structure of classification, data tuples are represented as samples, examples, or objects. The data tuples analyzed to create the model collectively form the training data set. The individual tuples making up the training set are represented as training samples and are selected from the sample population.

Because the class label of every training sample is provided, this process is also defined as supervised learning. In unsupervised learning, in which the class labels of the training samples are unidentified, and the various classes to be understand cannot be known in advance.