Predictive Model Evaluation Task

A Predictive Model Evaluation Task is an model evaluation task for a predictive model that assesses the performance metrics of a predictive model to determine how well it represents or predicts some target reality.

• Context:
  • Task Input: Predictive Model, Evaluation Dataset, Model Evaluation Metrics
    • Optional Input: Baseline Model Performance, Performance Thresholds, Model Confidence Scores
  • Task Output: Model Performance Report, Model Quality Assessment, Improvement Recommendations
  • Task Performance Measure: Evaluation Completeness, Assessment Validity, Reproducibility
  • ...
  • It can typically assess Model Accuracy through performance metric calculation.
  • It can typically identify Model Weaknesses through error pattern analysis.
  • It can typically compare Model Versions through standardized benchmark execution.
  • It can typically validate Model Robustness through stress test execution.
  • It can typically detect Model Bias through fairness assessment protocols.
  • ...
  • It can often facilitate Model Selection through comparative evaluation processes.
  • It can often provide Model Improvement Insights through failure analysis.
  • It can often implement Model Certification Standards through regulatory compliance checks.
  • It can often support Deployment Decisions through production readiness assessment.
  • ...
  • It can range from being an Interpolation Model ... to being an Extrapolation Model ..., based on ...
  • It can range from being an Offline Model Evaluation Task to being an Online Model Evaluation Task, depending on its evaluation timing.
  • It can range from being a Classification Model Evaluation Task to being a Ranking Model Evaluation Task to being an Estimation Model Evaluation Task, depending on its model output type.
  • It can range from being a Quantitative Model Evaluation Task to being a Qualitative Model Evaluation Task, depending on its assessment approach.
  • It can range from being a Single-metric Model Evaluation Task to being a Multi-metric Model Evaluation Task, depending on its performance dimension count.
  • ...
  • It can incorporate Statistical Tests for confidence interval calculation.
  • It can utilize Visualization Techniques for performance interpretation.
  • It can generate Performance Profiles for model behavior characterization.
  • It can maintain Evaluation History for model evolution tracking.
  • ...
• Examples:
  • Model Evaluation Task Types, such as:
    • Predictive Model Evaluation Tasks, such as:
      • Classification Accuracy Evaluation Task for class assignment correctness.
      • Regression Error Evaluation Task for numerical prediction precision.
    • Statistical Model Evaluation Tasks, such as:
      • Goodness-of-fit Evaluation Task for distribution alignment assessment.
      • Hypothesis Testing Task for model assumption validation.
    • Application-specific Model Evaluation Tasks, such as:
      • Recommendation Model Evaluation Task for user preference prediction.
      • Anomaly Detection Model Evaluation Task for outlier identification.
  • Model Evaluation Task Implementations, such as:
    • Cross-validation Evaluation Tasks, such as:
      • K-fold Cross-validation Task for generalization assessment.
      • Leave-one-out Evaluation Task for small dataset evaluation.
    • Production Evaluation Tasks, such as:
      • A/B Testing Evaluation Task for comparative live assessment.
      • Shadow Model Evaluation Task for parallel performance monitoring.
    • Time-based Evaluation Tasks, such as:
      • Temporal Validation Task for time-series prediction.
      • Concept Drift Detection Task for model degradation monitoring.
  • ...
• Counter-Examples:
  • Model Fitting Tasks, which focus on parameter optimization rather than performance assessment.
  • Algorithm Evaluation Tasks, which assess algorithm properties rather than specific model instances.
  • Feature Evaluation Tasks, which analyze input feature quality rather than model performance.
  • Data Quality Evaluation Tasks, which assess dataset properties instead of model capabilities.
  • Model Training Tasks, which create predictive models rather than evaluate them.
• See: Model Design Task, Model Performance Evaluation, Model Artifact, Homeostatic Model Assessment, Overfitting, ROC Analysis, Resubstitution Estimate, Resubstitution Error, Holdout Evaluation, Bootstrap Sampling, Performance Metric Selection, Model Validation Strategy, Predictive Model Creation, Predictive Model Evaluation Synchronization.

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References

2017

• (Webb, 2017) ⇒ Geoffrey I. Webb. (2011). “Model Evaluation.” In: (Sammut & Webb, 2017) p.683
  • QUOTE: Model evaluation is the process of assessing a property or properties of a model. (...)

    here are many metrics by which a model may be assessed. The relative importance of each metric varies from application to application. The primary considerations often relate to predictive efficacy — how useful will the predictions be in the particular context it is to be deployed. Measures relating to predictive efficacy include Accuracy, Lift, Mean Absolute Error, Mean Squared Error, Negative Predictive Value, Positive Predictive Value, Precision, Recall, Sensitivity, Specificity, and various metrics based on ROC analysis (...)

    When assessing the predictive efficacy of a model learned from data, to obtain a reliable estimate of its likely performance on new data, it is essential that it not be assessed by considering its performance on the data from which it was learned. A learning algorithm must interpolate appropriate predictions for regions of the instance space that are not included in the training data. It is probable that the inferred model will be more accurate for those regions represented in the training data than for those that are not, and hence predictions are likely to be less accurate for instances that were not included in the training data. Estimates that have been computed on the training data are called resubstitution estimates. For example, the error of a model on the training data from which it was learned is called resubstitution error.

     Algorithm evaluation techniques such as cross-validation, holdout evaluation, and bootstrap sampling are designed to provide more reliable estimates of the accuracy of the models learned by an algorithm than would be obtained by assessing them on the training data.

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