Recall
Recall is a classification metric measuring the proportion of actual positives that were correctly identified by the model. High recall is critical in medical diagnosis and other applications where missing true positives is costly.
Understanding Recall
Recall is a classification metric that measures the proportion of actual positive instances that a model correctly identifies, calculated as true positives divided by the sum of true positives and false negatives. High recall means the model catches most of the relevant cases, which is critical in applications where missing a positive instance carries severe consequences. In cancer screening, high recall ensures that most patients with the disease receive follow-up care; in security threat detection, it means most genuine threats are flagged. Recall exists in a fundamental tension with precision, as lowering the classification threshold to catch more positives inevitably increases false alarms. The F1 score provides a balanced summary by computing the harmonic mean of precision and recall. Domain experts carefully calibrate this tradeoff based on the relative costs of missed detections versus false positives in their specific application.
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.