Unsupervised Learning
Unsupervised learning is a machine learning approach where models discover patterns and structure in data without labeled examples. Clustering, dimensionality reduction, and anomaly detection are common unsupervised tasks.
Understanding Unsupervised Learning
Unsupervised learning trains models on data without labeled outcomes, tasking them with discovering hidden patterns, structures, and relationships autonomously. Unlike supervised learning, there are no correct answers to learn from; instead, algorithms identify natural groupings, reduce dimensionality, or learn data distributions. Key techniques include clustering algorithms like K-Means that group similar items, dimensionality reduction methods like PCA and autoencoders that compress data representations, and density estimation approaches. Unsupervised learning powers recommendation engines, customer segmentation in marketing, anomaly detection in cybersecurity, and topic modeling in document analysis. Self-supervised learning, which creates its own labels from unlabeled data, has blurred the boundary between unsupervised and supervised paradigms. The ability to extract value from unlabeled data makes unsupervised learning essential, given that the vast majority of real-world data lacks annotations.
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.