What is Regression in Machine Learning with Examples?

Regression is one of the most commonly used statistical methods for estimating or investigating the relation between a set of independent and dependent variables. Typically, regression models are used to predict a continuous value. For instance,  predicting the prices of a house given the features of the house, such as size, price, location, and more.

In this post, we will delve deeper into the concept of regression models in machine learning, specifically the various terminologies used here, types of regression models, and the various applications of regression.

Table of Contents:

1. What is Machine Learning?

2. What is Regression in Machine Learning?

3. Types of Regression Models in Machine Learning

4. Terminology Used in Regression Analysis 

5. Regression Algorithms

6. Characteristics of Regression

7. Examples of Regression

8. Metrics to Evaluate Regression Analysis

9. Advantages and Disadvantages of Regression

10. Conclusion

11. FAQs

What is Machine Learning?

Machine Learning (ML) is a specific branch of Artificial Intelligence and computer science that contains several algorithms to solve various real-world problems.

Put simply, machine learning focuses on using data and algorithms to enable AI to work the same way humans learn, thus gradually improving its accuracy.

What is Regression in Machine Learning?

A regression model in machine learning refers to a supervised machine-learning technique used primarily to predict the value of the dependent variable for both new and unseen data. The concept models the connection between the features inputted and the target variable, thus allowing for the easy prediction of numerical values.

Regression in machine learning also involves training a particular set of algorithms to find different patterns that identify the distribution of each data point. These patterns allow the model to make more accurate predictions for new input values.

Types of Regression Models in Machine Learning

Regression models in machine learning are statistical techniques that help to model the connection between one or more independent variables (predictors) and a dependent variable (response).

Below are different types of regression models in ML, each meant for specific scenarios and data types.

1. Linear Regression

A linear regression model is used when the connection between the dependent variable and the independent variables is considered to be linear. It is most suitable for continuous numerical data and when the response variable can be predicted using a straight line.

2. Multiple Regression

The multiple regression model is similar to linear regression but involves multiple independent variables. It is used when the response variable depends on more than one predictor variable or multiple independent variables. The concept of multiple linear regression should be used when there are multiple independent variables involved.

3. Nonlinear Regression

In most cases, the relationship between the dependent variable and independent variable(s) follows a nonlinear pattern. This relationship offers some flexibility in modeling a wide range of functional forms.

4. Polynomial Regression

This is a type of regression that is used when the relationship between the dependent and independent variables can be made better approximated by a polynomial function instead of a straight line. It is most suitable when data follows a curvilinear pattern.

5. Ridge Regression

This is another type of regression in machine learning that is utilized to avoid overfitting or diminish the complexity of a show. It is an essentially regularized form of linear regression that addresses multicollinearity issues (high correlation between predictors).

Terminology used in Regression Analysis

Wondering about the key terminologies related to regression analysis in machine learning?

Here are the most common ones-

• Predictor/Independent Variable

These are the specific factors that influence the response variable and are used to predict its values.

• Response/Dependent Variable

Also known as the target variable, it is one of the primary factors to predict or understand in regression.

• Outliers

Outliers refer to observations with very low or high values compared to others, which can potentially impact results and should, therefore, be avoided.

• Underfitting and Overfitting

Overfitting occurs when an algorithm performs well on training but poorly on testing. In contrast, underfitting indicates poor performance on both datasets.

• Multicollinearity

This is a term used when there is a high correlation among independent variables, which can further complicate the ranking of influential variables.

Regression Algorithms

There are multiple types of regression algorithms available. Here are some of the common ones discussed below-

1. Linear Regression

Linear regression is the simplest yet commonly used statistical model. It assumes a linear relationship between the independent and dependent variables, which means that any change in the dependent variable is directly proportional to the change in the independent variables.

2. Support Vector Regression (SVR)

SVR is another common type of regression algorithm that is dependent on the support vector machine (SVM) algorithm. It is primarily used for classification tasks.

3. Polynomial Regression

This is an algorithm for modeling nonlinear relationships between the dependent and independent variables. It counts up polynomial terms to the linear regression model to predict more complex relationships.

4. Random Forest Regression

This regression method uses multiple decision trees to predict the target value.

5. Decision Tree Regression

It is yet another type of regression algorithm that builds a decision tree to predict the target value. It has a unique structure consisting of nodes and branches.

Characteristics of Regression

Below are the key characteristics of the regression

a. Model Complexity

Regression models, in general, range from simple linear models to more complex nonlinear models. In such a scenario, the choice of model complexity primarily depends on the complexity of the relationship between the input features and the target variable.

b. Continuous Target Variable

The concept of regression is based on predicting continuous target variables that highlight various numerical values. Examples of this include predicting house prices or estimating the total recovery times for patients.

c. Error Measurement

The basis of evaluation for most regression models is their ability to lessen the error between the predicted and actual values of the target variable. Some of the common error metrics are mean squared error (MSE), mean absolute error (MAE), and root mean squared error (to be discussed later).

d. Interpretability

It is a regression model concept that varies based on the algorithm that is used. For instance, simple linear models are quite interpretable, whereas more complex models may not be so easy to interpret in general.

Examples of Regression

Here are some examples of regression

• To predict a house’s price given its features
• Predicting the impact of SAT/GRE scores on college admissions
• To predict sales based on input parameters
• Predicting the weather
• Predicting a person’s age or nationality
• To predict whether the stock prices of a company will increase in the future
•  

Metrics to Evaluate Regression Analysis

In this section, we will discuss some of the most common regression evaluation metrics to help you assess the effectiveness of your model.

1. Mean Squared Error

MSE is a statistic used to check or identify the quality of regression models. Here, you calculate the average squared error between the target variable and its projected value. The MSE score will be lower if the model fits the data better.

2. Mean Absolute Error(MAE)

MAE is a simple metric that calculates the absolute difference between actual and predicted values. The MAE you get in regression analysis is in the same unit as the output variable and is most robust to outliers.

3. Root Mean Squared Error

As the name suggests, root mean squared error is nothing but the square root of the average squared difference. While RMSE is similar to MAE, it takes the square root of the average squared difference.

Advantages and disadvantages of regression

Here are some of the advantages and disadvantages of regression that you need to know about-

Advantages

• Helps in Identifying the Relationship

The key benefit of regression analysis is that it helps in identifying both the nature and strength of the relationship between the dependent and independent variables.

• Predictive Analysis

Regression analysis can be instrumental for predictive analysis. It helps in predicting the value of the dependent variable based on the values of the independent variables.

• Decision Making

Regression analysis in machine learning is very useful for decision-making as it offers a quantitative assessment of the relationship between variables.

Disadvantages

• Sensitive to Outliers

One disadvantage of regression analysis is that it is sensitive to outliers, which can impact the result of the analysis.

• Overfitting

There could be instances of overfitting in regression analysis, which occurs when the regression model is too complex and fits the training data too closely. This leads to poor performance on new data.

• Linearity Assumption

Another downside of regression analysis is that it assumes a linear relationship between the dependent and independent variables. If this assumption is violated, the analysis’s results may not be accurate.

Conclusion

Regression in machine learning is a concept that helps us understand how different factors relate to each other and how they can influence an outcome. By using various algorithms, it allows you to spot patterns in data and accordingly use these patterns to make predictions for the latest situations.

The importance of regression in machine learning is huge. Regression techniques are essential for uncovering relationships within data and building useful predictive models for a wide range of enterprise use cases, such as sales forecasts and risk analysis.

If you’re a student, industry professional, or academician who wants to learn more about machine learning, IEEE is a one-stop solution for all your needs.

Check out these blogs and courses about regression in machine learning to enhance your knowledge of regression analysis and make the most of available resources.

FAQ

1. What is a regression in machine learning with an example?

Regression in machine learning is a technique to find how a dependent variable is changing with reference to independent variables. For example- it helps predict continuous/real values such as salary, temperature, age, price, etc.

2. What is regression in simple terms?

Regression is simply a statistical approach that you can use to analyze the connection between a dependent variable (target variable) and one or more independent variables (predictor variables).

3. What are the different regression types?

Among various regression models in machine learning are Linear Regression, Ridge Regression, Polynomial Regression, Lasso Regression, Logistic Regression and ElasticNet Regression.

4. What is the difference between regression and classification?

While regression is used to predict continuous values, classification helps categorize data. However, both are supervised learning tasks in machine learning.

5. Why is it called regression?

The term Regression for statistical approaches refers to the tendency of extreme data values to “revert” to the overall mean value.