Binary classification is a type of machine learning algorithm that categorizes data into one of two distinct classes. The output is a binary outcome, that is, either 'Yes' or 'No', 'True' or 'False', 'Positive' or 'Negative', etc. This makes it easier to make effective decisions and predictions based on past data. It is one of the simplest and most common types of classification problem.
How Binary Classification works
In binary classification, a model is trained on a set of input data along with their corresponding class labels, where the labels are of two types, generally denoted as 0 and 1. The model learns the underlying patterns in the data that determine its class, and uses this learning to classify new, unseen data.
The algorithm used for binary classification could be logistic regression, decision trees, support vector machines, etc. Once the model is trained, it uses a decision boundary derived from the training data to classify new instances. If the data point falls on one side of the boundary, it is classified into one category and if it falls on the other side, it is classified into the other category.
The performance of a binary classification model is often evaluated using metrics such as accuracy, precision, recall, F1 score, and the area under the Receiver Operating Characteristic (AUC-ROC) curve.
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