Linear Regression vs Logistic Regression in Machine Learning

Linear regression and logistic regression are two of the simplest machine learning algorithms. This article discusses linear regression vs logistic regression with examples to find the similarities and differences between the two algorithms.

Linear Regression vs Logistic Regression in Machine Learning

Before getting into the details, let us first look at a summary of linear regression vs logistic regression.

Linear regression	Logistic Regression
We use linear regression to predict a continuous dependent variable using the independent variables.	In logistic regression, the dependent variable is bivariate having values 0 and 1.
In linear regression, we try to fit a straight line to produce a linear function.	In logistic regression, we try to fit a sigmoid curve to produce an activation function.
We use linear regression for regression tasks.	Due to the discrete nature of the target variable, logistic regression is used for classification tasks.
In linear regression, we use least square estimation for accuracy estimation.	In logistic regression, we use maximum likelihood estimation.
In linear regression, the independent variables can be collinear.	In logistic regression, there should be no collinearity between the independent variables.

What is Linear Regression?

Linear regression in machine learning is a supervised learning technique that we use to predict continuous dependent variables with the help of independent variables. Here, if we have a single independent variable, we term it a simple linear regression. If there are two or more independent variables in the regression task, we term the method as multiple regression analysis.

In linear regression, we are given a set of independent variables with their dependent variable. Our goal is to find the best-fit line that we can use to predict the dependent target variable using the independent variables. By calculating the best-fit line, we try to establish a linear relationship between the independent variables and the dependent variable.

What is Logistic Regression?

Logistic regression is also a supervised machine-learning technique. In logistic regression, we try to predict a categorical target variable with the help of independent variables. In most cases, there are only two discrete categorical target variables in logistic regression. Due to this, logistic regression problems are often termed classification problems. 

Logistic regression is based o the maximum likelihood estimation concept. Here, we train a sigmoid curve that predicts the probability of a data point belonging to a specific class. Then, we use a threshold probably score to assign the data point to a specific class. Overall, the logistic regression algorithm takes an input data point, processes it using an activation function, and produces a binary output.

Linear Regression vs Logistic Regression Example

There are many examples where we can use linear regression and logistic regression in the real world. 

We use linear regression for predicting continuous variables. Hence, we can use this algorithm for the following example tasks.

• House Prices prediction: We can use linear regression to predict the price of a house based on factors like its size, number of rooms, and location.
• Sales forecasting: Linear regression can be used to predict future sales of a product based on its past sales data.
• Climate modeling: We can use linear regression to study the relationship between temperature and other weather variables, such as humidity or wind speed.
• Financial analysis: We can use linear regression to analyze the relationship between a company’s stock price and its financial performance, such as its revenue or earnings.

On the other hand, logistic regression is used for an entirely different set of tasks. Following are some of the examples where we can use logistic regression.

• Spam detection: We can use logistic regression to classify emails as spam or not spam based on the content of the email.
• Credit risk assessment: Logistic regression can be used to determine the likelihood of a customer defaulting on a loan based on their credit history and other relevant factors.
• Customer churn prediction: We can use logistic regression to predict the likelihood of a customer leaving a subscription service, based on their usage patterns, complaints, and other relevant factors.
• Fraud detection: We can use logistic regression to detect fraudulent transactions, based on various factors such as transaction amount, location, and type of transaction.

When to Use Linear Regression vs Logistic Regression?

Although linear regression and logistic regression both are regression algorithms, we use them in entirely different situations.

• If there is a linear relationship between the independent variables and the dependent variable, we use linear regression to predict the output. Here, the target variable or the dependent variable must be a continuous variable such as age, price, percentage, etc,
• If we need to predict a binary target variable, we use the logistic regression algorithm. Most of the time, logistic regression is used in binary classification tasks.

Conclusion

In this article, we had a discussion on linear regression vs logistic regression to analyze the difference and similarities between the two algorithms. We also discussed when to use linear regression vs logistic regression along with some examples.

To read more about machine learning, you can read this article on classification vs regression in machine learning. You might also like this article on KNN regression numerical example. 

I hope you enjoyed reading this article. Stay tuned for more informative articles. 

Happy Learning!