Linear and logistic regression are excellent statistical techniques used as machine learning algorithms to understand the relationship between features and labels in machine learning. In this article, I will take you through the difference between linear regression and logistic regression in machine learning.

## Difference Between Linear Regression and Logistic Regression

#### Linear Regression

** Linear regression** is a statistical technique used as a Machine Learning algorithm to understand the relationship between a dependent and independent variable. This is a supervised learning algorithm that is used in regression based problem statements such as predicting future sales, future stock prices, etc.

It is a great algorithm for regression analysis, but it is not as powerful as other regression algorithms because it is affected by outliers because it assumes that the dataset is *normally distributed* without having any Outliers. So, whenever you use linear regression, make sure that the dataset is normally distributed without having any outliers.

#### Logistic Regression

** Logistic regression** may look like another regression algorithm, but it is a classification algorithm. So, like linear regression, it also falls under the category of supervised learning but unlike linear regression, it is used for classification.

Logistic regression is one of the most powerful machine learning algorithms that can be used for binary classification problems such as classifying emails or SMS as spam or not spam.

So hopefully so far you have understood some differences between linear regression and logistic regression. In the section below, I will present you with a detailed comparison between linear regression and logistic regression.

Linear Regression | Logistic Regression |
---|---|

It is used in the problems of regression. | It is used in the problems of classification. |

Outputs a continuous variable. | Outputs a categorical variable such as 1 and 0. |

Used when the dependent and independent variable are linearly correlated. | No such conditions are required in this algorithm. |

It can output any amount of value. | It only outputs 0 and 1. |

Best to use when there are no outliers in the dataset. | Best to use when the problem statement is based on binary classification. |

Example: stock price prediction | Example: Spam detection |

I hope you liked this article on the difference between logistic and linear regressions in machine learning. Feel free to ask your valuable questions in the comments section below.

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