Model Evaluation Error Type

A Model Evaluation Error Type is an evaluation error type that is a statistical error type characterizing model assessment mistakes in model evaluation procedures that compromise model validation.

• AKA: Model Assessment Error Type, Statistical Model Error Category, Model Validation Flaw Type.
• Context:
  • It can typically manifest through Data Leakage via train-test contamination and information leak.
  • It can typically involve Overfitting to Test Set using repeated evaluation and test set optimization.
  • It can typically include Improper Cross-Validation through data dependency violation and temporal leak.
  • It can typically feature Distribution Shift via dataset mismatch and domain difference.
  • It can typically exhibit Metric Misuse through inappropriate measure and threshold bias.
  • ...
  • It can often result from Poor Split Strategy through non-random partition and stratification failure.
  • It can often arise from Label Leakage via target contamination and feature engineering error.
  • It can often stem from Selection Bias using cherry-picking results and p-hacking.
  • It can often emerge from Temporal Leakage through future information use and lookahead bias.
  • ...
  • It can range from being a Data-Related Model Evaluation Error Type to being a Methodology-Related Model Evaluation Error Type, depending on its error source.
  • It can range from being a Training Model Evaluation Error Type to being a Testing Model Evaluation Error Type, depending on its occurrence phase.
  • It can range from being a Subtle Model Evaluation Error Type to being an Obvious Model Evaluation Error Type, depending on its detection difficulty.
  • It can range from being a Common Model Evaluation Error Type to being a Rare Model Evaluation Error Type, depending on its frequency.
  • It can range from being a Critical Model Evaluation Error Type to being a Minor Model Evaluation Error Type, depending on its impact severity.
  • ...
  • It can invalidate Model Performance Claims through inflated metrics.
  • It can mislead Model Selection via biased comparisons.
  • It can waste Development Resources through false positives.
  • It can damage Research Credibility via irreproducible results.
  • It can require Evaluation Redesign through methodology correction.
  • ...
• Example(s):
  • Data Leakage Model Evaluation Error Types, such as:
    • Train-Test Leakage where training samples appear in test set.
    • Feature Leakage where target information contaminates input features.
    • Preprocessing Leakage where normalization uses test statistics.
    • Temporal Leakage where future data influences past predictions.
  • Validation Model Evaluation Error Types, such as:
    • Overfitting to Validation Set through excessive tuning.
    • Data Snooping by peeking at test data.
    • Multiple Testing Problem without bonferroni correction.
    • Adaptive Data Analysis causing false discovery.
  • Sampling Model Evaluation Error Types, such as:
    • Sample Selection Bias with non-representative samples.
    • Survivorship Bias excluding failed cases.
    • Class Imbalance Ignorance using accuracy on skewed data.
    • Stratification Error in cross-validation splits.
  • Metric Model Evaluation Error Types, such as:
    • Wrong Metric Choice for task objective.
    • Single Metric Reliance ignoring trade-offs.
    • Threshold Selection Bias optimizing decision boundary.
    • Average Misinterpretation hiding subgroup performance.
  • ...
• Counter-Example(s):
  • System Evaluation Error Types, which involve system testing flaws rather than model assessment errors.
  • Model Training Errors, which are optimization problems rather than evaluation mistakes.
  • Implementation Bugs, which are code errors rather than methodology flaws.
• See: Model Evaluation Method, ML Evaluation Error, Cross-Validation, Data Leakage, Overfitting, Statistical Error, Model Validation.

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