Types Of Regression Analysis In Machine Learning

Regression Analysis is a powerful statistical tool used in data analysis to explore relationships between variables. It can find scope in almost all important sectors, such as economics, finance, healthcare, and environmental science. There are various types of regression analysis, such as logistic, multiple, linear, polynomial, and time regression. Each type of regression analysis has its advantages, and it is crucial to select the most suitable approach to address the specific research or decision-making challenges at hand.

In this post, we will learn about various types of regression analysis and their use cases. You need to go through the complete article to understand the complete topic better.

Table of Contents

What Is Regression Analysis?

Regression Analysis is a statistical method used in data analysis to examine the relationship between one or more independent variables and a dependent variable. Independent variables are also called predictors, and dependent variables are known as target variables or outcomes. The main objective of the regression analysis is to analyze the relationship, which helps make predictions and identify patterns or derive conclusions based on the data.

It helps to answer what changes will be observed in the dependent variable with the change in independent variables. In regression, we analyze data through graphs and check the variable that best fits the data points in the plot.

Why Use Regression Analysis?

There are various applications of regression analysis in data analysis. Regression analysis predicts a possible outcome based on the continuous variable. Regression data analysis determines the relationship between dependent and independent variables. It helps to predict or forecast based on historical data, which helps to anticipate some of the future outcomes like sales, stock prices, etc.

It helps to find trends in data and predict real or continuous values. It can help make predictions more accurately, like sales predictions, marketing trends, and weather conditions.

It also helps understand the relationships between different variables and analyze how changes in one variable affect the other. It helps in hypothesis testing and helps researchers determine if specific factors affect the favorable outcome.

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Types Of Regression Analysis

There are different types of Regression analysis in data science and machine learning. Regression analysis analyzes the effect of the independent and dependent variables. Let us discuss major important types of regression.

Linear Regression

Linear regression is a statistical method used to analyze and model the relationship between dependent and independent variables. It aims to find the best-fitting linear equation that describes the relationship. In short, it is used for predictive analysis. It is used to solve different regression problems in machine learning.

Linear regression represents the linear relationship between independent variables and dependent variables. It represents independent variables on the X-axis and dependent variables on the Y-axis.

There are two types of Linear Regression. One with only a single input variable, also known as simple linear regression. If there is more than one input variable, it is called Multi-linear Regression.

Y = aX + b

Y = dependent variable

X = Independent Variable

a,b= linear coefficients

Logistic Regression

This is another form of regression analysis that solves different classification problems in machine learning. The dependent variables in these types of problems are in the discrete form, like 0 and 1. They work with boolean values such as true or false, yes or no, etc. It works on probability concepts. Regression uses them to calculate the relationship between the dependent and independent variables. It should be kept in mind that the size of the data to be taken is large and that there is no correlation between any of the independent variables in the data.

Unlike linear regression, it predicts the possibility that an observation belongs to one of the two classes. It uses a logistic function to map any real-valued number into a value between 0 and 1. These two numbers represent the probability. Here, given the representation of the sigmoid function in logistic regression.

Here, f(x)= output between 0 and 1

x= input of the function

e= base of the algorithm

After giving the input, it provides us with a s curve.

Logistic regression is used in many fields. Some of the major fields are given here.

Medical fields for predicting disease.
Machine learning
Marketing predictions

Polynomial Regression

The polynomial regression model is used to model the non-linear dataset using a linear model. This type of regression is used when the relationship between the independent and dependent variables is not linear. It exists in curve form. This regression technique satisfies the polynomial equation of various forms, such as cubic, quadratic, or other higher-order equations.

The main objective of polynomial regression is to find a curve that best fits the data and minimizes the distance between the predicted and actual data values. While using polynomial regression, it is very important to consider the degree of polynomials.

Y = b0+b1x+ b2x2+ b3x3+…..+ bnxn.

Here, Y is the target output.

b0, b1, b2,… bn are regression coefficients.

Support Vector Regression

A support vector is a type of machine-learning algorithm that is used in regression analysis. It does not focus on minimizing the errors between the actual and predicted values. At the same time, it focuses on fitting a regression line around which a specified margin of error is allowed.

The main objective of the Support vector algorithm is to consider the maximum number of data points inside the boundary line. The aim is to produce the maximum number of data points in a hyperplane. It can handle both linear and non-linear relationships between the two variables with the help of kernel functions.

Kernel functions: It helps transform data into a higher dimensional space, making capturing the non-linear pattern possible.

Boundary Line: Two lines at a distance in the hyperplane create the margin for the data points.

Support Vectors: The data points closest to the hyperplane, and the opposite class are known as support vectors.

These regression models are used when dealing with datasets that contain too much noise and are complex. It is commonly used in fields like finance to predict the stock price, model complex relationships, etc.

Decision Tree Regression

A decision tree is a widely used machine learning algorithm that is used for both classification and regression tasks. Classification is a supervised learning algorithm that predicts the correct label for the given input data. It is a graphical representation of the decision-making process. It is like an inverted tree where each node represents the outcome of the test, and each leaf node represents a decision or a prediction.

The construction of a tree takes place through the recursive partitioning of data into independent variables. After the construction of tree data, predictions are made from root to leaf node based on feature tests.

The decision tree makes visualization easy. It is generally used for classification tasks like spam mail, disease diagnosis, and sentiment analysis. They help to classify data into various categories or classes based on their features. It is also used in regression, credit scoring, medical diagnosis, image recognition, agriculture, education, quality control, recommendation systems, etc.

Ridge Regression

Ridge regression is a specialized version of linear regression. It is designed to minimize the problems of overfitting and multicollinearity. In this regression model, regularization terms are added to the linear functions.

Where:

n is the number of observations

p is the number of independent variables

Yi is the observed value.

Yi is the predicted value.

Bj is the coefficient of the jth independent variable in the linear regression model.

Λ (lamda) is the regularization parameter or penalty term. It controls the regularization applied to the model.

The ridge regression function aims to find the coefficients’ value (Bj). The value of Λ determines the strength of regularization. The larger the value, Λ more substantial the regularization and coefficient values.

Types of Regression FAQs

What is the Regression Analysis?

Regression Analysis is a statistical method used in data analysis to examine the relationship between one or more independent variables and a dependent variable.

What are the major types of Regression Analysis?

The significant types of regression analysis are linear regression analysis, logistic, polynomial, support vector, decision tree, ridge regression analysis.

What are the three regression models?

The three regression models are linear, non-linear, and multiple linear.