Clustering
Clustering is an unsupervised learning technique that groups similar data points together without predefined labels. Common clustering algorithms include K-Means, DBSCAN, and hierarchical clustering.
Understanding Clustering
Clustering is an unsupervised learning technique that discovers natural groupings within data without requiring labeled examples, making it invaluable when the underlying structure of a dataset is unknown. K-Means, perhaps the most widely known algorithm, partitions data into a specified number of clusters by minimizing within-cluster distances, while DBSCAN identifies clusters of arbitrary shape based on density. Hierarchical clustering builds nested trees that reveal multi-scale structure. Real-world applications include customer segmentation for targeted marketing, document clustering that organizes text collections by topic, and anomaly detection that flags data points outside established groups. The choice of distance metric and number of clusters significantly impacts results, and techniques like the elbow method and silhouette scores help determine optimal configurations. Modern approaches incorporate deep learning through autoencoders to learn better feature representations before clustering, bridging unsupervised and representation learning.
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.