Definition
Scores quantify how well a machine learning model performs against specific metrics such as accuracy, precision, or reward. In reinforcement learning, scores indicate cumulative rewards, while in classification, they may represent probability confidence levels. These values are critical for comparing different models, tuning hyperparameters, and determining the best candidate solutions during optimization processes.
Summary
A score is a numerical value representing the quality, confidence, or fitness of a model’s prediction or solution.
Key Concepts
- Metric Evaluation
- Confidence Level
- Reward Signal
- Optimization Target
Use Cases
- Evaluating classification accuracy
- Tracking RL agent progress
- Ranking search results