Logistic Regression
Logistic regression is a statistical technique that is commonly used for problems involving binary classification, where the outcomes are dichotomous, such as yes/no or true/false. Unlike linear regression, which predicts continuous outcomes, logistic regression predicts the likelihood of a given input falling into a certain class. This is performed by using the logistic (or sigmoid) function, which converts the answer of a linear equation into a probability value between 0 and 1. Forecasting the likelihood of a patient having a specific ailment in the medical sector, predicting customer turnover in marketing, and determining credit scores in finance are all common applications. Despite being very simple to execute and analyze, and being effective for linearly separable data, logistic regression requires a linear relationship.
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