Linear Regression and Logistic Regression are the popular machine learning algorithms that come under the supervised learning technique. Let’s discuss the differences between linear and logistic regression.
What is Linear Regression?
Linear Regression is one of the most popular and straightforward machine learning algorithms. It belongs to the family of supervised learning methods used for cracking regression problems. One of the most common uses of linear regression is to signify the continuous dependent variable through independent variables. It basically aims to encounter the most suitable fit line that can perfectly forecast the output for the continuous dependent variable.
What is Logistic Regression?
Logistic regression is one of the most prevalent machine learning algorithms. It also belongs to supervised learning techniques. This algorithm is generally used for regression problems as well as classification problems. It is basically used to forecast the categorical dependent variable through independent variables.
Difference between Linear and Logistic Regression
| S.No. | Linear Regression | Logistic Regression |
|---|---|---|
| 1. | It is used to anticipate the continuous dependent variable through the available set of independent variables. | It is used to anticipate the categorical dependent variable utilising the group of independent variables. |
| 2. | Linear Regression is mostly used for evaluating regression problems. | Logistic regression is mostly preferred to solve classification problems. |
| 3. | In the case of linear regression, we can easily anticipate the value of continuous variables. | In the case of logistic regression, we can easily anticipate the values of categorical variables. |
| 4. | Here we prefer the least square estimation method. | Here we prefer the maximum likelihood estimation method. |
| 5. | In case of linear regression, the output should be a continuous value. | In case of logistic regression, the output should be categorical. |
| 6. | The bond between dependent variable and independent variable should be linear. | Here it is not compulsory to have a linear relationship between the dependent and independent variable. |
| 7. | There may be collinearity between the independent variables. | There should not be collinearity between the independent variables. |
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