Hierarchical Clustering
Hierarchical clustering is an unsupervised method that builds a tree-like hierarchy of nested clusters. It can be agglomerative (bottom-up merging) or divisive (top-down splitting) and produces a dendrogram visualization.
Understanding Hierarchical Clustering
Hierarchical clustering is an unsupervised machine learning technique that builds a tree-like structure of nested clusters from data points. Unlike k-means clustering, it does not require specifying the number of clusters in advance and instead produces a dendrogram that shows how clusters merge or split at different levels of similarity. Agglomerative (bottom-up) approaches start with each point as its own cluster and iteratively merge the closest pairs, while divisive (top-down) approaches start with one cluster and recursively split it. Hierarchical clustering is widely used in bioinformatics for gene expression analysis, in market research for customer segmentation, and in natural language processing for organizing document collections. The choice of distance metric and linkage criterion significantly affects the resulting cluster structure.
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.