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.
Understanding Active Learning
Active learning addresses one of the most practical bottlenecks in machine learning: the cost of labeled data. Rather than requiring a massive fully annotated dataset upfront, an active learning system strategically selects the most informative unlabeled examples and requests annotation for only those samples. This is especially valuable in domains like medical imaging or legal document review, where expert annotation is expensive and time-consuming. The model typically identifies data points near its decision boundary or where its uncertainty is highest, then queries a human oracle for the correct labels. This iterative loop of training, querying, and retraining dramatically reduces the total annotation effort while maintaining or even improving accuracy. Active learning pairs well with techniques like semi-supervised learning and transfer learning to build high-performing models with minimal labeled data.
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.
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.
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.