Difference between Linear Regression and Logistic Regression?

Linear regression is a statistical technique that is used to find a relationship between two variables. It is used for predicting continuous values, such as the price of stocks or the number of cars sold in a given month. It is often used in the social sciences to analyze data. Linear regression, also called “ordinary least squares” (OLS), is best suited for data that are continuous and that have a linear relationship.

Logistic regression is a statistical technique that assigns probabilities and predicts categorical values. It is used for predicting binary outcomes, such as whether someone will buy or not buy a product. It is used in the social sciences to model binary outcomes. Logistic regression, also called “maximum likelihood”, is best suited for data that are categorical and have an exponential relationship.

Linear RegressionLogistic Regression
A linear regression is a statistical calculation that works on the relationship between one or more Dependent Variables and one or more Independent Variables.  Logistic Regression is a supervised classification model. This means that the algorithm will take input variables and use them to predict discrete values for output variables.  
Linear regression is a statistical technique which predicts the value of a given input by an integer number.  Logistic Regression allows us to predict the probability that a variable will take on either a 1 or 0 value.  
No activation function is used  The activation function is used to transform a linear regression equation into a logistic regression equation.  
Linear regression assumes that the dependent variable follows a normal or gaussian distribution.  Logistic regression assumes a binomial distribution of the dependent variable.  
The estimation is based on the least square method  ML is based on the idea of maximum likelihood estimation.  
Linear regression can be used to estimate the price of houses depending on, for example, the size.  Logistic Regression is a statistical technique used to work out the probability of an event happening. For example, making a quick decision whether or not some tissue is malignant  

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