1) Linear regression requires variables to obey a normal distribution, while logistic regression does not require variable distribution.
2) Linear regression requires that the dependent variable is a continuous numerical variable, while logistic regression requires that the dependent variable is a categorical variable.
3) Linear regression requires a linear relationship between the independent variable and the dependent variable, while logistic regression does not require a linear relationship between the independent variable and the dependent variable
4) Logistic regression analyzes the relationship between the probability that the dependent variable takes a certain value and the independent variable, while linear regression does not require a linear relationship between the independent variable and the dependent variable. It is a direct analysis of the relationship between the dependent variable and the independent variable
In a word,
logistic regression and linear regression actually have a lot in common. The biggest difference is that their dependent variables are different. model (generalized
linear
model). The models in this family are basically the same in form. The difference is that the dependent variable is different. If it is continuous, it is multiple linear regression, and if it is a binomial distribution, it is logistic regression. The dependent variable of logistic regression can be binary or multi-class, but binary is more commonly used and easier to interpret. Therefore, the most commonly used in practice is the two-class logistic regression.