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Prerita Agarwal

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23 Jul, 2024 @ 01:30 PM

Introduction

Regression is a data mining technique that is used to model the relationship between a dependentvariable and some independentvariables. This relationship is then used to predict future values of the independent variables.

In this blog, we will discuss Regression in Data Mining in detail. We will discuss the different types of Regression in Data Mining. We will also see some common applications of Regression. In the next section, we will discuss Regression in detail.

What is Regression in Data Mining

As already told in the previous section, Regression in Data mining is a technique to establish a relationship between a dependent variable and some independent variables. The dependent variable is also called the response variable, and the independent variable is also called the predictor variable. We will make use of these terms quite often in the upcoming sections.

Let's try to understand it better with an example. Let's say the price of a car depends on its horsepower, number of seats and its top speed. In this example the car becomes the dependent variable whereas the horsepower, number of seats and the top speed are all independent variables. If we have a data record containing previous records of the price of cars with their features, we can build a regression model to predict the price of a car depending on its horsepower, number of seats and the top speed.

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Types of Regression in Data Mining

Now that we know about Regression, let us see the various types of Regression in data mining. Although many regression models exist, some of the most common models are discussed below.

Linear Regression

Linear Regression is the most basic type of Regression in data mining. This regression model assumes that the dependent variable has a linear relationship with the independent variables. Like any other regression model, linear Regression aims to find the best-fitting curve for predicting future values. The general equation of a linear regression model is given by:

y = a1.x + a2 + e

In this equation, a1 is the slope, a2 is the intercept, and e is the error quantity. The slope indicates the rate of change of the dependent variable w.r.t the difference in the independent quantities. In contrast, the intercept indicates the value of the dependent variable when the independent variable is zero. The graph of a linear regression model looks like this.

Polynomial Regression

In polynomial Regression, the relation between the dependent and the independent variable is assumed as a polynomial of nth degree where n is in the range from [2, infinity].In real life, the relationship between variables is generally polynomial. Higher the degree of the polynomial, the more accurate it is.

The general equation of polynomial Regression looks like this.

y = a0 + a1x + a2x^2 + a3x^3

This is a polynomial equation of 3rd degree. Here a0, a1, a2, and a3 are the coefficients. The sample graph of a polynomial regression is given below.

Logistic Regression

Logistic Regression is generally used when the dependent variable is binary (true or false)or multinomial (low, medium, high). Logistic Regression is a prediction-based technique to predict the probability of the dependent variable based on the values of the independent variable.

The following equation gives the equation of a logistic regression model.

y = 1/(1 + exp(-z))

In this equation, y is the probability of the dependent variable taking the particular value, and z is the combination of independent variables. The value of y lies between 0 and 1. The graph of logistic Regression looks like this.

Ridge Regression

Ridge regression is a technique that adds a penalty term to the ordinary least squares cost function. Ridge regression is used to tackle the issue of multicollinearity. Multicollinearity occurs when the independent variables themselves correlate them. By compensating the coefficients in Ridge regression, the effect of multicollinearity can be reduced and make the model more stable.

Lasso Regression

Like the Ridge regression, Lasso Regression also adds a penalty term to the ordinary least squares cost function. LASSO stands for Least Absolute Shrinkage and Selection Operator. As the definition suggests, we use Lasso regression to minimize the effects of the coefficient.

The significant difference between Lasso and Ridge regression is how much they can affect the coefficients. While Lasso Regression can effectively reduce the coefficients to zero, ridge regression cannot.

Difference Between Regression, Classification, and Clustering in Data Mining

Parameter

Regression

Classification

Clustering

Objective

Predict a continuous outcome

Assign data points to predefined categories

Group similar data points together

Output

Continuous values

Discrete classes

Unlabeled clusters

Supervision

Supervised learning

Supervised learning

Unsupervised learning

Example

Predicting house prices

Classifying emails as spam or not spam

Segmenting customers based on behavior

Algorithm types

Linear regression, polynomial regression

Decision trees, logistic regression, support vector machines

K-means, hierarchical clustering, DBSCAN

Evaluation

Mean squared error (MSE), R-squared

Accuracy, precision, recall, F1-score

Silhouette score, Daviesâ€“Bouldin index

Applications of Regression in Data Mining

Regression plays an important role in data mining. Some of the major applications of regression are discussed below.

Finance - Regression models are widely used in the finance industry to analyze money-related metrics. It can be used to study and predict the future impact of factors like GDP (Gross Domestic Product).

Marketing - Regression is also used in the marketing industry to understand consumer behavior and helps the business to predict and identify its target goals.

Future Projection - With the help of Regression and current data stats, we can project future trends and make data-driven predictions.

Healthcare - Regression also plays a vital role in the medical research industry. It is used to examine medicines, predict the future number of patients of a particular disease, etc., which is useful for research purposes.

Frequently Asked Questions

What is the difference between Regression and Classification in Data Mining?

Although Regression and classification are pretty much the same, they still have significant differences. While Regression is used to predict continuous values, the classification technique is used to predict values of discrete variables.

How do we evaluate the performance of a particular regression method?

Generally, the performance of a particular regression method is based on how well it can fit the curve represented by the data. To check the goodness-of-fit of the data, we use errors like Root mean squared error (RMSE).

What is the difference between single and multiple regression?

In single Regression, the value of the dependent variable depends solely on a single variable. On the other hand, in multiple Regression, the dependent variable is affected by a combination of more than one variable.

Conclusion

In this article, we discussed Regression in Data Mining. We discussed the types of Regression in data mining with their graphs. In the end, we concluded by discussing some applications of Regression and some frequently asked questions.

So now that you know what Regression in Data Mining is, you can refer to similar articles.