Model Performance Metrics

Definition ∞ Model performance metrics are quantitative measures used to evaluate the effectiveness, accuracy, and efficiency of machine learning models within various applications, including those in blockchain and decentralized systems. These metrics assess how well a model achieves its intended purpose, such as predicting market movements or identifying fraudulent transactions. Examples include accuracy, precision, recall, and F1-score, providing a standardized way to compare and optimize model behavior. They are crucial for assessing the utility of AI in digital asset contexts.
Context ∞ In the realm of decentralized learning and AI applications within crypto, news reports often reference model performance metrics when discussing the capabilities and reliability of new algorithms or predictive tools. These metrics are central to evaluating the viability of AI-driven solutions for tasks like market analysis, risk assessment, or protocol optimization. Understanding these measures helps assess the real-world utility and trustworthiness of AI projects operating on blockchain infrastructure.