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.
Understanding Bayesian Network
Bayesian networks are powerful probabilistic graphical models that represent complex relationships between variables as a directed acyclic graph, where nodes represent variables and edges encode conditional dependencies. Unlike many machine learning approaches that learn opaque correlations, Bayesian networks explicitly model causal and probabilistic relationships, making them particularly valuable for causal inference and decision-making under uncertainty. In healthcare, Bayesian networks model the relationships between symptoms, diseases, and test results to support clinical diagnosis. In risk assessment, they help quantify the probability of cascading failures in complex systems. Each node in the network stores a conditional probability table that captures how it depends on its parent nodes, and inference algorithms like variable elimination propagate evidence through the graph. Bayesian networks are especially useful when data is limited or when domain knowledge needs to be incorporated into the model, complementing data-driven approaches like deep 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.
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.
Boosting
Boosting is an ensemble method that trains models sequentially, with each new model focusing on correcting the errors of previous ones. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.