Supervised Learning
Supervised learning is a machine learning approach where models learn from labeled training data — input-output pairs. It is the most common ML paradigm, powering classification and regression tasks.
Understanding Supervised Learning
Supervised learning is the most established machine learning paradigm, where models learn to map inputs to outputs using labeled training data containing input-output pairs. The algorithm adjusts its internal parameters to minimize the difference between its predictions and the known correct answers, measured by a loss function. Common supervised tasks include classification (assigning categories like spam detection or image recognition) and regression (predicting continuous values like house prices or temperature forecasts). Algorithms range from simple linear models and support vector machines to complex deep neural networks and ensemble methods like XGBoost. The quality and quantity of training data fundamentally determine supervised model performance, making data annotation and collection critical steps in any supervised learning pipeline.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.