2Class

2class is a term used in machine learning and statistics to denote a binary or two-class classification problem. In a 2class task, each instance is assigned to one of two classes, commonly labeled 0 and 1 or negative and positive. The goal is to learn a function f that maps a feature vector x to a predicted label y in {0,1} using a training set of labeled examples.

Common methods include logistic regression, linear discriminant analysis, support vector machines, decision trees, random forests, gradient boosting, and neural networks. Many algorithms produce probabilistic outputs, which can be thresholded to yield a class label, and probability estimates are often evaluated with metrics such as ROC AUC.

Evaluation uses a confusion matrix and metrics: accuracy, precision, recall, F1 score, and area under the ROC or PR curves. In practice, datasets are split into training and testing sets; cross-validation is often used to estimate performance. Class imbalance is a frequent issue; techniques include class-weighted learning, resampling, and synthetic minority oversampling (SMOTE).

Common applications include spam detection, medical diagnosis (e.g., presence or absence of a disease), fraud detection, and any scenario requiring a yes/no decision. Challenges in 2class problems include overlapping class distributions, feature selection, overfitting, and calibration of probability estimates.

Historically, binary classification forms the core of statistical decision theory and supervised learning, with early methods such as logistic regression and linear discriminant analysis remaining widely used.