Evaluation Metric
An evaluation metric is a quantitative measure used to assess model performance on a given task. Common metrics include accuracy, precision, recall, F1 score, AUC-ROC, and perplexity.
Understanding Evaluation Metric
An evaluation metric is a quantitative measure used to assess how well a machine learning model performs on a given task. Common metrics include accuracy, precision, recall, F1 score, AUC-ROC for classification, and mean squared error or R-squared for regression. The choice of metric depends on the problem domain and business requirements; for example, in medical diagnostics, recall may be prioritized to minimize missed diagnoses, while in spam filtering, precision might matter more. Metrics are calculated on validation or test sets during cross-validation to estimate generalization performance. Beyond standard metrics, specialized measures exist for tasks like object detection (mean average precision), natural language processing (BLEU, ROUGE), and generative AI (FID score for image quality). Selecting the right evaluation metric is a critical step in model development and drives decisions during hyperparameter tuning.
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.