Logistic Regression Calculator
In statistics, the logistic model, also known as the logit model, serves as a fundamental tool for modeling the probability of a specific class or event occurrence. This versatile model finds application across various domains, from determining pass/fail outcomes to predicting win/lose scenarios, alive/dead conditions, or healthy/sick states. Its utility extends to multiclass event modeling, such as identifying objects in images, where each object's presence is assigned a probability between 0 and 1, summing up to one.
Originally prominent in the biological sciences during the early twentieth century, logistic regression has found widespread use in social sciences and beyond. It particularly shines when the dependent variable, often termed the target, is categorical in nature.
Logistic regression operates by predicting the probability of an event through fitting data to a logistic curve, thus making it a form of binomial regression within the broader framework of generalized linear models. This method leverages predictor variables, which can be either numerical or categorical. For instance, one might predict the likelihood of a person experiencing a heart attack within a specified timeframe based on factors such as age, sex, and body mass index.
The versatility of logistic regression is evident in its extensive adoption across diverse fields. In the medical realm, it aids in predicting various health outcomes, while in social sciences, it facilitates understanding phenomena through statistical analysis. Moreover, logistic regression finds practical use in marketing endeavors, aiding in predicting customer behavior such as purchase propensities or subscription cessation.
References:
- Bishop, Christopher M.; Pattern Recognition and Machine Learning. Springer; 1st ed. 2006.
- Amos Storkey. (2005). Learning from Data: Learning Logistic Regressors. School of Informatics. Available on: http://www.inf.ed.ac.uk/teaching/courses/lfd/lectures/logisticlearn-print.pdf
- Cosma Shalizi. (2009). Logistic Regression and Newton's Method. Available on: http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf
- Edward F. Conor. Logistic Regression. Website. Available on: http://userwww.sfsu.edu/~efc/classes/biol710/logistic/logisticreg.htm
- In this tool we used LogisticRegression, Class
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