Classification

Classification is a supervised machine learning task where a model learns to assign predefined labels to new data points. Training data contains both inputs and corresponding class labels, allowing the model to capture patterns and make predictions on unseen data.

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Types

Binary classification: two categories (e.g., fraud detection: “fraud” vs “legit”).
Multiclass classification: multiple categories (e.g., image recognition: cat, dog, horse).
Multilabel classification: multiple labels per instance (e.g., a movie tagged as “comedy” and “romance”).

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Common algorithms

Logistic regression
Decision trees and random forests
Support Vector Machines (SVM)
Deep learning models (CNNs, RNNs)

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Applications

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Classification lies at the heart of supervised learning because so many real-world tasks boil down to making decisions between categories. From deciding whether a medical scan shows signs of disease to filtering offensive content online, classification enables AI systems to transform raw features into actionable predictions.

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Different algorithms bring different strengths. Logistic regression is simple, fast, and interpretable, making it popular in regulated industries. Random forests and gradient boosting excel at handling tabular data with nonlinear patterns. Neural networks, especially deep architectures, shine in complex domains like vision and speech. The choice often depends not only on accuracy but also on interpretability, scalability, and cost.

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One important challenge is dealing with imbalanced data, where one class (like “fraudulent transaction”) is much rarer than others. Standard accuracy becomes misleading in such cases, so practitioners rely on metrics like precision, recall, and F1-score. Ultimately, classification is not just about labeling—it’s about ensuring that the predictions are reliable enough to support critical decisions.

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Reference

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.