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Difference Between Classification and Regression
In data mining, there are two major predication problems, namely, classification and regression. The most basic difference between classification and regression is that classification algorithms are used to analyze discrete values, whereas regression algorithms analyze continuous real values.
The output variable must be either continuous nature or real value. The output variable in classification has to be a discrete value. In contrast, the output variable in regression must be either continuous in nature or real values.
In this article, we will discuss all the important differences between classification and regression. Let's start with some basics of Classification and Regression so that it becomes easier to understand how they are different from each other.
What is Classification?
Classification is the process of finding a model that represents and differentiate 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 set of training records, i.e., data objects whose class label is familiar.
Classification is one of the most important concepts in data mining because it defines a process of assigning predefined class labels to instances depending on their attributes. Classification is a method that predetermined to make the analysis of high datasets effective.
What is Regression?
Regression is a type of supervised machine learning approach which can be used to forecast any continuous−valued attribute. Regression gives some business organization to explore the target variable and predictor variable associations. Thus, regression is one of the essential tools to explore the data that can be used for monetary forecasting and time series modeling.
We can use regression to perform classification. For this, it uses two methods, namely, division and prediction. In the case of division, the data is divided into regions situated on class, whereas in prediction, some formulae are used to predict the output value of the class.
Regression can predict some dependent datasets. Regression also supports methods to predict variables, but there are certain restrictions and assumptions like independence of variables, inherent normal distributions of the variables, etc.
Difference between Classification and Regression
The following table highlights all the important differences between Classification and Regression −
|Classification gives out discrete values.||Regression gives continuous values.|
|Given a group of data, this method helps group the data into different groups.||It uses the mapping function to map values to continuous output.|
|In classification, the nature of the predicted data is unordered.||Regression has ordered predicted data.|
|The mapping function is used to map values to pre−defined classes.||It attempts to find a best fit line. It tries to extrapolate the graph to find/predict the values.|
|Example include Decision tree, logistic regression.||Examples include Regression tree (Random forest), Linear regression|
|Classification is done by measuring the accuracy.||Regression is done using the root mean square error method.|
The most significant difference between Classification and Regression is that Classification provides a predictive model that predicts new data in discrete labels with the help of historic data, whereas Regression predicts the data in continuous values.
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