Model Evaluation Error Type
(Redirected from Model Assessment Error Type)
Jump to navigation
Jump to search
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:
- Sampling Model Evaluation Error Types, such as:
- 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.
- ...
- Data Leakage Model Evaluation Error Types, such as:
- 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.