The parameters obtained in linear regression
Answers
Answered by
0
Introduction to Logistic Regression
Logistic Regression is a type of predictive model that can be used when the target variable is a categorical variable with two categories – for example live/die, has disease/doesn’t have disease, purchases product/doesn’t purchase, wins race/doesn’t win, etc. A logistic regression model does not involve decision trees and is more akin to nonlinear regression such as fitting a polynomial to a set of data values. Logistic regression can be used only with two types of target variables:
A categorical target variable that has exactly two categories (i.e., a binary or dichotomous variable).
A continuous target variable that has values in the range 0.0 to 1.0 representing probability values or proportions.
As an example of logistic regression, consider a study whose goal is to model the response to a drug as a function of the dose of the drug administered. The target (dependent) variable, Response, has a value 1 if the patient is successfully treated by the drug and 0 if the treatment is not successful. Thus, the general form of the model is:
Logistic Regression is a type of predictive model that can be used when the target variable is a categorical variable with two categories – for example live/die, has disease/doesn’t have disease, purchases product/doesn’t purchase, wins race/doesn’t win, etc. A logistic regression model does not involve decision trees and is more akin to nonlinear regression such as fitting a polynomial to a set of data values. Logistic regression can be used only with two types of target variables:
A categorical target variable that has exactly two categories (i.e., a binary or dichotomous variable).
A continuous target variable that has values in the range 0.0 to 1.0 representing probability values or proportions.
As an example of logistic regression, consider a study whose goal is to model the response to a drug as a function of the dose of the drug administered. The target (dependent) variable, Response, has a value 1 if the patient is successfully treated by the drug and 0 if the treatment is not successful. Thus, the general form of the model is:
Answered by
0
Introduction to Logistic Regression
Logistic Regression is a type of predictive model that can be used when the target variable is a categorical variable with two categories – for example live/die, has disease/doesn’t have disease, purchases product/doesn’t purchase, wins race/doesn’t win, etc. A logistic regression model does not involve decision trees and is more akin to nonlinear regression such as fitting a polynomial to a set of data values. Logistic regression can be used only with two types of target variables:
A categorical target variable that has exactly two categories (i.e., a binary or dichotomous variable).
A continuous target variable that has values in the range 0.0 to 1.0 representing probability values or proportions.
As an example of logistic regression, consider a study whose goal is to model the response to a drug as a function of the dose of the drug administered. The target (dependent) variable, Response, has a value 1 if the patient is successfully treated by the drug and 0 if the treatment is not successful.
Logistic Regression is a type of predictive model that can be used when the target variable is a categorical variable with two categories – for example live/die, has disease/doesn’t have disease, purchases product/doesn’t purchase, wins race/doesn’t win, etc. A logistic regression model does not involve decision trees and is more akin to nonlinear regression such as fitting a polynomial to a set of data values. Logistic regression can be used only with two types of target variables:
A categorical target variable that has exactly two categories (i.e., a binary or dichotomous variable).
A continuous target variable that has values in the range 0.0 to 1.0 representing probability values or proportions.
As an example of logistic regression, consider a study whose goal is to model the response to a drug as a function of the dose of the drug administered. The target (dependent) variable, Response, has a value 1 if the patient is successfully treated by the drug and 0 if the treatment is not successful.
Similar questions