ML Model Confidence Scoring Task

A ML Model Confidence Scoring Task is a confidence scoring task that is an ML uncertainty quantification task (for prediction reliability assessment).

• AKA: Model Uncertainty Scoring Task, Prediction Confidence Task, ML Reliability Assessment Task.
• Context:
  • Task Input: ML Model Predictions, Model Internal State
    • Optional Input: Validation Data, Calibration Set
  • Task Output: Confidence Scores, Uncertainty Estimates
  • Task Performance Measure: Confidence Quality Metrics such as calibration error, uncertainty correlation, and confidence accuracy
  • ...
  • It can typically compute Prediction Confidence using ML uncertainty methods.
  • It can typically identify Low-Confidence Predictions for ML prediction review.
  • It can often calibrate Confidence Thresholds through ML validation data.
  • It can often distinguish Aleatoric Uncertainty from epistemic uncertainty.
  • It can often be performed by ML Model Confidence Scoring Systems with ML uncertainty quantification methods.
  • ...
  • It can range from being a Point-Estimate Confidence Task to being a Distribution-Based Confidence Task, depending on its ML confidence representation.
  • It can range from being a Model-Agnostic Confidence Task to being a Model-Specific Confidence Task, depending on its ML model dependency.
  • It can range from being a Binary Confidence Task to being a Continuous Confidence Task, depending on its ML confidence granularity.
  • It can range from being a Single-Model Confidence Task to being an Ensemble Confidence Task, depending on its ML model count.
  • It can range from being a Post-Hoc Confidence Task to being an Integrated Confidence Task, depending on its ML confidence timing.
  • ...
  • It can be implemented using ML Model Confidence Scoring Systems with ML confidence algorithms.
  • It can utilize ML Model Confidence Scoring Systems for ML prediction reliability assessment.
  • It can enable Selective Prediction through ML confidence filtering.
  • It can integrate with ML Pipelines for ML uncertainty monitoring.
  • ...
• Example(s):
  • Classification Confidence Tasks, such as:
    • Multi-Class Confidence Scoring for category prediction uncertainty.
    • Binary Classification Confidence for decision boundary uncertainty.
  • NLP Confidence Tasks, such as:
    • NER Confidence Scoring for entity extraction reliability.
    • Translation Confidence Task for language model uncertainty.
  • Domain Confidence Tasks, such as:
    • Medical Diagnosis Confidence for clinical prediction reliability.
    • Financial Prediction Confidence for market forecast uncertainty.
  • ...
• Counter-Example(s):
  • Deterministic Prediction Task, without confidence estimation.
  • Hard Classification Task, lacking probability outputs.
  • Rule-Based Decision Task, without uncertainty quantification.
• See: Confidence Scoring Task, ML Uncertainty Task, Prediction Reliability Task, Model Calibration Task, Uncertainty Quantification Task, Probabilistic Prediction Task.

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