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Binary Classification is a supervised machine learning task where the goal is to classify the elements of a dataset into one of two possible categories or groups, each called class, hence it is a special case of a classification where there are exactly two classes.

For instance, it is used to solve problems such as fraud detection (fraud or legit). The model’s output is a binary decision, often represented as 0 or 1, true or false, indicating the predicted class for each input instance. Evaluation metrics such as accuracy, precision, recall, and F1 score are commonly used to assess the performance of binary classification models.