Tag: fairness
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Beyond Accuracy: Evaluating Machine Learning With Robustness Metrics
Introduction: Why Accuracy Isn’t the Whole Picture For decades, the success of machine learning models has been largely measured by a single metric: accuracy. A model that hits 95% accuracy sounds impressive, but this figure can mask how a model behaves under real-world conditions. Data shift, adversarial inputs, biased datasets, and varying deployment environments can…
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Beyond Accuracy: Evaluating Machine Learning with Robustness Metrics
Rethinking Evaluation: Why Accuracy Isn’t Enough For decades, accuracy has stood as the standard yardstick for machine learning success. A model boasting 95% accuracy signals strong performance on its test data, yet it can still fail in real-world scenarios characterized by noise, shifts in data distribution, or adversarial manipulation. As AI systems move from laboratories…
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Beyond Accuracy: Robustness Metrics for Evaluating ML Models
Rethinking Model Evaluation: Why Accuracy Isn’t Enough For decades, selecting and deploying machine learning models has largely revolved around a single figure: accuracy. A model reporting 95% accuracy may seem exceptional, implying strong predictive power and reliability. But real-world systems operate in messy, changing environments where data distributions shift, adversaries probe weaknesses, and edge cases…