Logistic Regression in AI/ML: A Detailed Explanation with Examples

Logistic regression is a supervised machine learning algorithm that is used to predict the probability of an event occurring. It is a classification algorithm, which means that it can be used to classify data into two or more categories. <h1>Understanding Logistic Regression</h1> At its core, Logistic Regression is a statistical method used for binary classification, predicting the probability of an input belonging to a particular class. It’s widely applied in various domains, including medical diagnosis, spam detection, and credit scoring. The logistic regression model is a linear model, but the output is not a linear function of the input. Instead, the output is a probability, which is a value between 0 and 1. The logistic regression model is made up of two parts: the linear model and the sigmoid function. The linear model is a linear combination of the input variables. The sigmoid function is a nonlinear function that maps the output of the linear model to a probability. <a href="https://medium.com/@chinna202023/logistic-regression-in-ai-ml-a-detailed-explanation-with-examples-da8bc7ad22bd">Website</a>