What is Binary Classification in AI
Binary classification in AI refers to a type of supervised learning task where the objective is to categorize input data into one of two distinct classes or labels. This process involves training a model using a labeled dataset, where each instance is associated with one of the two classes. Common examples of binary classification tasks include spam detection (classifying emails as spam or not spam), sentiment analysis (determining if a review is positive or negative), and medical diagnosis (identifying whether a patient has a certain condition). The effectiveness of a binary classification model is often evaluated using metrics such as accuracy, precision, recall, and the F1 score, which provide insights into its performance in distinguishing between the two classes.
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