Comparison of Classification vs. Regression

Comparison of Regression vs. Classification

Comparison of Classification vs. Regression. Data mining and machine learning are used to solve two primary prediction problems: classification and regression. The process of identifying or developing a model or function that aids in the separation of data into numerous categorical classes (i.e. discrete values) is known as classification. In classification, data is classified into distinct labels based on input factors, and the labels are then predicted for the data.

The “IF-THEN” rules might be used to show the derived mapping function. When data may be split into binary or many discrete labels, the classification method is used to solve the problem. Consider the following scenario: we want to forecast the likelihood of Team A winning a match based on certain previously recorded characteristics. Then there would be two labels, one for Yes and the other for No.

Comparison of Regression vs. Classification
Comparison of Classification vs. Regression

The process of creating a model or function for converting data into continuous real values rather than utilising classes or discrete values is known as regression. Based on past data, it may also determine dispersion movement. The skill of a regression predictive model must be expressed as an error in those predictions since it predicts a quantity. Let’s look at a similar scenario in regression, where we’re looking for the likelihood of rain in specific locations based on some previously collected factors. Then there is the possibility of rain.

Comparison between Regression and Classification:

BasicMapping Function is used for mapping of values to predefined classes.Mapping Function is used for mapping of values to continuous output.
Involves prediction ofDiscrete valuesContinuous values
Nature of the predicted dataUnorderedOrdered
Method of calculationby measuring accuracyby measurement of root mean square error
Example AlgorithmsDecision tree, logistic regression, etc.Regression tree (Random forest), Linear regression, etc.

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