Application of Regression in Data Mining
20 Pages Posted: 25 Nov 2020 Last revised: 7 Jan 2021
Date Written: November 20, 2020
Regression is one of a data mining technique which is used to anticipate a range of continuous values(numeric) for a given data set. In other words, regression is used to observe the cost of a product or service from given other variables. It is widely used in business marketing, financial & trend forecasting, modelling of environment and so on. Classification is also similar to regression but it is used to predict a discrete set of values not continuous. Regression is used at the situation when the output is a continuous value like salary, weight, height etc. A kind of demographic activity for evaluating the interrelation between the subordinate variables and more than one absolute variables(predictors) is known as regression analysis which helps us to know how much the value of the subordinate variable is varying by the independent variable. The key terms involved in regression are dependent & independent variable, outliers, multicollinearity, underfitting and overfitting. In this paper, the regression concepts are going to be discussed and various kinds of regression techniques available and their uses in the real world. The main desire is to clear about the regression concept and its importance in this paper.
Keywords: Kinds of regression, Predictor variable, Forward selection, Supervised classification, Penalty function.
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