Supervised learning is demonstrated using logistic regression. It is used to compute or forecast the likelihood of a binary (yes/no) event occurring. An example of logistic regression could be applying machine learning to determine if a person is likely to be infected with COVID-19 or not.
Logistic regression is classified into three types: binary, multinomial, and ordinal.
Linear Regression and Logistic Regression: What's the Difference? Logistic regression is used to handle classification problems, whereas linear regression is used to solve regression problems. Linear regression produces continuous results, whereas logistic regression produces discrete results.
Log Loss is the cost function used in Logistic Regression.
Basic assumptions for logistic regression include error independence, linearity in the logit for continuous variables, the absence of multicollinearity, and the absence of very influential outliers.