Logistic Regression
Logistic regression is a statistical method used for binary classification tasks, modeling the probability of a binary outcome based on one or more predictor variables. It uses a logistic function (sigmoid) to map linear combinations of inputs to probabilities between 0 and 1, making it interpretable and widely applicable in fields like medicine, finance, and social sciences. Despite its name, it is a classification algorithm, not a regression technique for continuous outcomes.
Developers should learn logistic regression when working on binary classification problems, such as spam detection, disease diagnosis, or customer churn prediction, due to its simplicity, efficiency, and interpretability. It serves as a foundational machine learning algorithm, often used as a baseline model before exploring more complex methods like neural networks or ensemble techniques, and is essential for understanding probabilistic modeling in data science.