Selective Prediction
A machine learning framework that allows a model to abstain from answering when uncertain. It uses a utility function to balance the reward for correct answers against the cost of incorrect ones, enabling an optimal trade-off between coverage and precision.
Core Facts
Timeline (last 90 days)
Selective prediction means that a model can choose to abstain when uncertain, handing the sample to humans or a subsequent process.
An abstention mechanism cannot rely solely on the model's self-assessment; it also requires external calibration methods or the variance of ensemble predictions from multiple models to estimate true uncertainty.
Selective Prediction is a formal research direction in machine learning whose core idea is to introduce a rejection option for models, allowing the model to output "I don't know" when confidence falls below a threshold.
In the selective prediction framework, the utility function is typically written as U = α·P(correct) - β·P(wrong), where α and β represent the reward weight for correct answers and the penalty weight for wrong answers, respectively.
Reliable abstention capability across datasets remains an unsolved problem, as methods that work on one dataset may fail on another.
Confidence-based routing is also referred to in academia and industry as 'selective prediction' or 'classification with a reject option,' with the core being granting the model the right not to answer.
Decision models focus on answering yes/no judgments, multiple-choice questions, and probability-scored rating-type problems.
All Facts (7)
Decision models focus on answering yes/no judgments, multiple-choice questions, and probability-scored rating-type problems.
75%UnverifiedSelective prediction means that a model can choose to abstain when uncertain, handing the sample to humans or a subsequent process.
50%UnverifiedSelective Prediction is a formal research direction in machine learning whose core idea is to introduce a rejection option for models, allowing the model to output "I don't know" when confidence falls below a threshold.
50%UnverifiedAn abstention mechanism cannot rely solely on the model's self-assessment; it also requires external calibration methods or the variance of ensemble predictions from multiple models to estimate true uncertainty.
50%UnverifiedIn the selective prediction framework, the utility function is typically written as U = α·P(correct) - β·P(wrong), where α and β represent the reward weight for correct answers and the penalty weight for wrong answers, respectively.
50%UnverifiedReliable abstention capability across datasets remains an unsolved problem, as methods that work on one dataset may fail on another.
50%UnverifiedConfidence-based routing is also referred to in academia and industry as 'selective prediction' or 'classification with a reject option,' with the core being granting the model the right not to answer.
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