Читать книгу Data Science For Dummies - Lillian Pierson - Страница 56

Supervised learning algorithms require that input data has labeled features. These algorithms learn from known features of that data to produce an output model that successfully predicts labels for new incoming, unlabeled data points. You use supervised learning when you have a labeled dataset composed of historical values that are good predictors of future events. Use cases include survival analysis and fraud detection, among others. Logistic regression is a type of supervised learning algorithm, and you can read more on that topic in the next section.

Survival analysis, also known as event history analysis in social science, is a statistical method that attempts to predict the time of a particular event — such as a mother’s age at first childbirth in the case of demography, or age at first incarceration for criminologists.