Understanding Logistic Regression
Logistic regression is a statistical approach utilized to examine the connection between one or more independent variables and a categolocal dependent variable. It is a type of regression analysis commonly applied in machine learning and predictive modeling.
In this blog post, I will explore the basics of logistic regression, how it works, and its applications.
What is Logistic Regression?
Logistic regression is a type of regression analysis used to model the probability of a binary outcome (i.e., yes or no, 1 or 0). Based on one or more inputt factors, it is used to forecast the chance that an event will occur. The likelihood of the event occurring, which can range from 0 to 1, is the logistic regression's output.
How Does Logistic Regression Work?
Llogistic regression works by modellingthe relationship between the input variables and the probability of the outcome variable. It uses a mathematical function called the logistic function (also known as the sigmoid function) to map the input variables to the output probability.
The logistic function is defined as:
P(y=1|x) = 1 / (1 + e^(-z))
where P(y=1|x) is the probability of the event occurring given the input variables, z is a linear combination of the input variables, and e is the base of the natural logarithm.
The linear combination of the input variables is represented by the following equation:
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z = b0 + b1x1 + b2x2 + b2x3 + ... + bnxn
where
b0 = the intercept,
b1, b2, ..., bn = the coefficients for the input variables x1, x2, ..., xn, respectively.
The logistic function maps the linear combination of input variables to a probability value between 0 and 1. If the probability is greater than or equal to 0.5, the event is predicted to occur (i.e., y=1), otherwise, it is predicted not to occur (i.e., y=0).
Applications of Logistic Regression
Conclusion
Logistic regression is a powerful statistical tool used for predictive modelling in various fields. Based on the input factors, it is used to forecast the likelihood that an event will occur. A straight-forward and basic model that can handle both binary and nominal outcomes is logistic regression. Finance, marketing, and healthcare all make extensive use of it. In addition to analysing the impact of various input factors on the result variable, logistic regression also gives a measure of the relevance of input variables in predicting the outcome variable.
Clinical Trials Biostatistician at 2KMM (100% R-based CRO) ? Frequentist (non-Bayesian) paradigm ? NOT a Data Scientist (no ML/AI/Big data) ? Against anti-car/-meat/-cash and C40 restrictions
2 年Mohan Krishna Dasari Thank you for your post! ?? For ages it's one of the very few articles recognizing the LR as the regression algorithm, modelling, as every GLM, the E(Y|X=x) which is always numeric (here, for Bernoulli conditional response the E() the probability of success), which can be used in many places, of course including classification (where the *logistic classifier* is built on the top of the numerical LR output). I thought you might find interesting also other applications, for example from my field (experimental research). As you mentioned, it's used for exploratory/descriptive assessment of the impact of covariates on the probability of success, and this way can be used also for inference about it (testing for various "contrasts", giving confidence intervals about them) https://www.dhirubhai.net/posts/adrianolszewski_logisticregression-regression-clinicaltrials-activity-7029478716317573120-Z4oc and https://www.dhirubhai.net/posts/adrianolszewski_logistic-regression-in-experimental-research-activity-7011319898316402688-GjNs