In data science, what method is used when a data example can only be classified as a 1 or 0?

Binary classification is the method used when a data example can be categorized into one of two distinct classes, typically represented as 0 or 1. This type of classification is essential for scenarios where the outcome can only fall into one of two possible categories, such as whether an email is spam or not, or whether a patient has a certain disease.

In binary classification, algorithms are trained on labeled data to understand the relationship between the input features and the binary outcome. The model outputs a probability that is then mapped to one of the two categories based on a chosen threshold, often set at 0.5.

The other options refer to different approaches not suited for binary outcomes. Multiclass classification is used when there are more than two classes to choose from. Regression is aimed at predicting continuous outcomes rather than categorical ones. Time series analysis deals with data points indexed in time order and is utilized for forecasting rather than binary classification tasks. Thus, binary classification is the appropriate term for this context.