Tag: model evaluation
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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…
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Modeling Strategies for Flexible Estimation of Crude Cumulative Incidence in Long Follow-Ups: Model Choice and Predictive Ability Evaluation
Introduction In clinical studies evaluating therapies for major diseases such as cancer, overall survival (OS) is a gold standard endpoint. Yet OS blends multiple failure causes and can obscure the actual burden of disease- or treatment-related events. In long follow-ups, competing risks (e.g., non-disease death) influence observed outcomes and make crude cumulative incidence (CIF) a…
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Modeling Strategies for Flexible Estimation of Crude Cumulative Incidence in Long Follow-Ups
Introduction: Why crude cumulative incidence matters in long follow-ups In clinical studies evaluating therapeutic interventions, overall survival is a well-established endpoint. However, crude cumulative incidence (CCI) offers a direct view of event probabilities when competing risks are present. When follow-up is prolonged, standard methods can misestimate CCI due to time-varying hazards and changing risk profiles.…