Logistic regression solves problems that require the target variable to be a discrete value / categorical target variable. For example, a person's gender (male or female) can be a discrete output variable.
Even though a continuous outcome variable generated in linear regression can be converted to a categorical variable, it is not advisable to do so as it can drastically reduce the precision of the results.
Logistic regression can be applied in any of the following situations:
When there is a nonlinear relationship between the predictor variables and the target variable
When the variance of errors is not constant
The fundamental principle behind the logistic regression algorithm (using the maximum likelihood estimation) is dissimilar to that of linear regression.
In its basic form, logistic regression estimates the probability of an event occurring. Logistic regression generally uses conditional maximum likelihood estimation. Parameters (w) that make the probability...
