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Evaluation methods

concept · part of Machine Learning

Methods for evaluating agent effectiveness:

• Benchmarking: compare against predefined standards or other agents
• A/B testing: run two versions (with/without changes) to measure performance differences
• Confusion matrix: display true positives, true negatives, false positives, false negatives (useful for classification)
• Cross-validation: evaluate on different data subsets to avoid overfitting and measure generalization
• Stress testing: evaluate under extreme conditions (large datasets, peak traffic) to identify bottlenecks

• Industry-standard benchmarking: For recommendation engines, compare your agent against a known industry-standard algorithm to evaluate accuracy and response time.

• Load testing: simulate high concurrency (e.g., hundreds of simultaneous users) to verify response times remain acceptable under peak demand.

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