Data Mining Tutorial

Data Mining Tutorial Association Rule in Data Mining Data Mining Process Cluster Analysis in Data Mining Data Cleaning in Data Mining Data Mining Algorithms Difference between Data Warehouse and Data Mining Data Mining Tools Data Pre-processing in Data Mining Major Issues in Data Mining Clustering in Data Mining Data Mining Steps Data Mining Applications Data Mining Functionalities Data Mining Architecture Data Mining Techniques Data Mining Tasks Regression in Data Mining KDD in Data Mining Outlier Analysis in Data Mining Data Cleaning in Data Mining

Regression in Data Mining
2021-02-04 11:01:12

Regression in Data Mining

Regression in Data Mining

Regression can be defined as a data mining technique that is generally used for the purpose of predicting a range of continuous values (which can also be called “numeric values”) in a specific dataset.

For example, Regression can predict sales, profits, temperature, distance and so on.

Applications of Regression

Regression is widely used in many businesses and industries. It is very popular also. Listed below are some of the applications of Regression:

The process of Regression often involves the predictor variable (the values that are identified by the users) and the response variable (the values that are to be predicted).

Regression in Data Mining

Types of Regression in Data Mining:

Two types of Regression can be observed in data mining. Those two types are given below:

  1. Linear Regression Model
  2. Multiple Regression Model
Regression in Data Mining

Linear Regression Model

Linear Regression is used mainly for the purpose of modeling the relationship between the two given variables. This is usually done by fitting a linear equation to perceive the data.

In addition to that, it can also be used for finding the mathematical relationship between the variables. It is the simplest form of Regression.

In various cases, when the outcome is a curved line, then the model is considered non- linear, and when a linear model is observed, the outcome will be a straight line.

The formula used for linear Regression is given below:

Y = bX + A

Where, Y is the model of linear function X, b is the slope of the line, and A is the intercept (which refers to the point where X crosses the y- axis).

The value of Y will increase or decrease in a manner that the value of X will change along with it in a linear manner.

Multiple Regression Model

Multiple Regression Model is generally used to explain the relationship between multiple independent or multiple predictor variables.

It can be considered as one of the most popular models for predictions in data mining.

In general, it uses two or more than two independent variables to predict an outcome for the users.

The formula that is used in the multiple regression model is given below:

Y = a0 + a1x1 + a2x2 + a3x3+ a4x4+ ….... +akxk + e

Where, Y is the response variable (the values that have to be predicted).

X1+X2+X3+X4+Xk are the independent predictors.

e is the random error in the above formula.

A0, A1, A3, A4, Ak are the regression coefficients.

Difference between Regression and Classification in Data Mining

Classification and Regression are two major prediction problems that are used in data mining. Both classification and Regression are similar, so it becomes difficult for the user to understand when to use it. Below given are the key points of differences between the classification and Regression in data mining:

Classification Regression
Classification is mainly used for allotting the given data into discrete categories.Regression is particularly used for the aim of predicting numeric or continuous values.
The nature of the predicted data is unordered.The nature of the predicted data is ordered.
In the method of classification, the calculations are usually done by measuring the accuracy.In the method of Regression, the calculations are usually done by using the root mean square error.
Classification can be further classified into the binary classifier and multi- class classifier.The algorithm can be further classified into linear Regression and non- linear Regression. 
Examples of classification are decision tree, logistic Regression and many others.The examples of Regression can be Random forest or Regression tree, linear regression, to name a few.

Conclusion:

Classification technique gives the users the predictive model or function used to predict the new data in discrete categories. That is done with the help of historic data. However, the regression method model uses continuous-valued functions, which predicts the data's outcome in continuous numeric form. Moreover, in the classification method, the nature of the predicted data is unordered, and in the regression model, it is ordered.