Classification

Classification is one of the most fundamental tasks in machine learning. Given a set of input features — pixel values, text tokens, sensor readings — a classification model predicts which discrete category the input belongs to. When there are exactly two possible classes (e.g., fraudulent vs. legitimate transaction), it is called binary classification. When there are three or more classes (e.g., identifying animal species from photos), it is multiclass classification. Some problems are multi-label, where an input can belong to several classes simultaneously.

A wide range of algorithms can perform classification. Logistic Regression, despite its name, is a simple and interpretable classifier. Decision Trees and Random Forests split data based on feature thresholds. Support Vector Machines find optimal decision boundaries between classes. Neural networks, from simple perceptrons to deep Convolutional Neural Networks, learn hierarchical feature representations that achieve state-of-the-art results on complex classification tasks. The choice of algorithm depends on dataset size, feature complexity, and interpretability requirements.

Classification models are evaluated using metrics like accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Accuracy alone can be misleading for imbalanced datasets — a model that always predicts 'not fraud' achieves 99.9% accuracy if only 0.1% of transactions are fraudulent, yet it is completely useless. For this reason, practitioners pay close attention to per-class metrics and use techniques like stratified sampling, oversampling, or cost-sensitive learning to handle class imbalance.