Logistic Regression is a type of supervised learning algorithm used in binary classification problems. It is a predictive modeling technique used to identify the relationship between a dependent variable and a set of independent variables. In logistic regression, the dependent variable is a binary variable that is either 0 or 1. It is used to predict the probability of an outcome occurring as a result of the combination of predictor variables (independent variables).
Logistic regression is based on the logistic function, which is a nonlinear transformation of linear regression. The logistic function has an S-shaped curve that can take any real-valued number and map it to a value between 0 and 1. The logistic function is used to transform the linear regression equation to the logistic regression equation. The output of the logistic regression equation can then be used to predict the probability of an event occurring.
Logistic regression has a number of advantages over other predictive modeling techniques. It is relatively simple to implement, has a low variance, and can be used to model non-linear relationships. Additionally, it can be used to identify important predictors of an outcome.
Logistic regression is widely used in the fields of health sciences, economics, and social sciences to identify and predict the probability of an outcome occurring. For example, it can be used to predict the probability of a person developing a disease based on their age, gender, and other factors. It can also be used to predict the probability of a person being in a certain income bracket based on their educational background and other factors.
References
Hecht, F. (2020). Logistic Regression: A Quick Introduction. Retrieved from https://towardsdatascience.com/logistic-regression-a-quick-introduction-3cdd42e7a5e
Bhatia, S. (2019). Logistic Regression: What It Is and How It Works. Retrieved from https://www.datacamp.com/community/tutorials/understanding-logistic-regression
Geron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media.