Logistic Regression vs. Linear Regression

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Difference Between Logistic Regression and Linear Regression

In logistics regression vs. linear regression, logistic regression evaluates the probability of an event occurring based on independent variables, and linear regression predicts the value of a dependent variable based on the independent variable.

Since the logistic regression estimates probability, the output will be a number between 0 and 1; the dependent variable is in binary form. In the case of linear regression, the dependent variable (response variable) is continuous.

Difference Between Logistic Regression and Linear Regression

Logistic regression vs linear regression in machine learning are algorithms to analyze data, samples, and situations and derive possible changes, scenarios or results.
In linear regression, the analysts seek the value of dependent variables, and the outcome is an example of a constant value. In the case of logistic regression, the outcome is categorical.
The value of the logistic regression outcome can be yes or no, 1 or 2, and true or false. In contrast, the linear regression outcomes are continuous values.

Comparative Table - Logistic Regression and Linear Regression

Particulars	Logistic Regression	Linear Regression
Definition	The method is used to model the probability of an event or outcome, predicting the category of the dependent variable (outcome variable).	The method is used to predict the value of the dependent variable when the dependent variable is continuous.
Purpose	The method uses independent variables to predict the categorical dependent variable.	The method uses independent variables to predict the continuous dependent variable.
Solves	Appropriate for classification problems.	Solves regression problems.
Output	The response variable is categorical, like yes or no, 1 or 2, and true or false.	The response variable is continuous.
Creates	Produces S-curve	Line of best fit
Linear relationship	A linear relationship between dependent and independent variables is not necessary.	A linear relationship between the dependent variable and independent variable.
Multicollinearity	Correlation between independent variables is not acceptable.	Correlation between independent variables is acceptable.
Methods used for the estimation of accuracy include	Maximum Likelihood Estimation	Least Square Method

What Is Logistic Regression?

Logistic regression application is popular in classification and predictive analytics. The method estimates the outcome in the form of probability based on independent variables. The logistic regression equation contains the dependent variable, independent variable, beta parameter, coefficient, and error component.

First, the best beta parameter or coefficient is estimated using the maximum likelihood estimation (MLE) in a way that results in the best fit. The calculation of the probability then follows it. The value of probability only comes between 0 and 1. To understand the binary classification, consider an example, if the probability is less than .5, the result will be 0, and if the probability is very close to 1, then the output will be 1.

Logistic regression is classified into three types, namely, binary, multinomial, and ordinal. In binary type, the dependent variable only comes out either 1 or 0, which means that the result is definite and only showcases one result; this could be true or false, yes or no, win or lose, success or failure but only one of them.

In the multinomial case, there is no quantitative significance in this type of regression outcome, representing three or more possible outcomes like type A, type B, or type C. The third one, ordinal type, is similar to multinomial but also possesses quantitative importance. The outcomes have multi-categories like good, better, and best, and each level has a score like 0, 1, 2, 3, etc. These interpret binary, multinomial, or ordinal logistic regression vs linear regression.

What Is Linear Regression?

Linear regression is a statistical technique attributing to predictive analytics. It is used to predict the quantitative value of a variable or future outcomes. The value of a dependent variable is evaluated based on the independent variable. The method utilizes the linear relationships between a dependent variable and one or more independent variables.

The dependent variable is modeled as a linear function containing regression parameters and a random error. The criterion, like the least square method, is used to determine the parameters from the data, which gives the best fit to the data. Suppose there exists more than one independent variable in the test. In that case, the process is called multiple linear regression, and this reason points to the explanation of multiple linear regression vs logistic regression.

The dependent variable is synonymous with a response, target, output, outcome, predicted, etc. At the same time, the independent variable is also known as the input variable, factor, explanatory variable, predictor variable, etc.

Linear regression use is popular in business, behavioral science, biology, and social science. On the other hand, logistics regression application is more popular in fields like machine learning and social sciences. In these wide categories of applications, if the predicted values should be probabilities rather than outcomes and the dependent variable is binary logistic regression is preferred over linear regression and forms the reason for interpreting when to use logistic regression vs linear regression.

Similarities

Both are regression models to analyze data, predict outcomes, and establish relationships.
Both models have vast applications in different fields of study, experiment, research, and surveys.
Both are well-designed and constructed machine learning algorithms, indicating the significance of going through linear regression vs logistic regression examples in machine learning.
Both algorithms apply dependent and independent variables in linear and logistic regression.
Both are branches of supervised learning study and research.