Faculty of Quantitative and Scientific Reasoning · Module F7-QS-08
Model Evaluation and Generalisation Discipline
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Faculty of Quantitative and Scientific Reasoning
Module F7-QS-08: Model Evaluation and Generalisation Discipline
Learning Objective
By the end of this module, you can identify data leakage in a train/test setup and describe its effect on reported accuracy; select an appropriate evaluation metric for a given task type (classification, regression, ranking); interpret confidence intervals on evaluation results to assess whether a performance difference is meaningful; and recognise when a model's evaluation environment is unrepresentative of its deployment environment.
1. Generalisation: The Core Claim
When you evaluate a model, you are testing a claim about generalisation: does the model perform well on inputs it has not been trained on, drawn from the same distribution as real deployment? A model that achieves high accuracy on its training data but fails on new inputs has not learned a general rule — it has memorised the training examples. This failure is called overfitting.
The evaluation discipline exists entirely to test whether a model generalises. All the methodological rules in this module — data splits, holdout sets, avoiding leakage — derive from this single goal: ensure that the evaluation measures performance on inputs that are genuinely unseen to the model.
Overfitting vs underfitting
Overfitting occurs when a model is too closely tailored to the training data: it captures noise and idiosyncrasies of the training set rather than the underlying pattern. It performs well on training examples and poorly on new ones. The gap between training performance and test performance is the diagnostic signal.
Underfitting occurs when a model is not complex enough to capture the underlying pattern. It performs poorly on training data and on test data. Unlike overfitting, underfitting is usually obvious immediately and is not a measurement error — the performance is low everywhere.
The evaluation discipline primarily protects against overfitting, because overfitting is the silent failure: high training performance creates false confidence.
2. Data Leakage
Data leakage occurs when information that would not be available at prediction time influences the model's training or evaluation. It causes inflated performance estimates that do not reflect real-world behaviour.
Training leakage occurs when test or validation data influences model training — either directly (test examples included in training) or indirectly (training features derived from statistics that include the test set). A common indirect form: computing feature normalisation parameters (mean, variance) over the entire dataset before splitting it. The model is then implicitly informed by the distribution of the test set.
Feature leakage occurs when a feature that is a proxy for the outcome label is included in the model but would not be available at prediction time. For example: including "outcome confirmed on day 3" as a feature when the model will be used to predict on day 1. The feature is causally downstream of the outcome — including it gives the model information it should not have.
Temporal leakage is a form of feature leakage specific to time-ordered data: using future data to predict past events during training or evaluation. A model trained on shuffled time-series data may inadvertently use future values as context for past events. Temporal leakage requires specific protections: time-ordered splits, where all training data precedes all validation data.
The diagnostic for leakage: evaluate whether performance is suspiciously high relative to theoretical limits or comparable published work, and whether the gap between training and test performance is unusually small. Perfect or near-perfect performance on genuinely difficult tasks is a strong signal of leakage, not a reliable result.
3. Train, Validation, and Test Splits
The standard evaluation protocol divides data into three parts:
Training set: used to fit the model. The model sees this data and adjusts its parameters in response.
Validation set: used during development to select hyperparameters and model architectures. The model does not train on this data, but the developer makes choices that are informed by validation performance. This indirect use means that repeated consultation of the validation set causes mild overfitting to validation.
Test set: used once, at the end of development, to report final performance. The test set should not influence any modelling decision. If evaluation on the test set reveals a problem, the correct response is to return to training and validation — not to re-tune and re-evaluate the test set.
Cross-validation is an alternative to a fixed validation set. In k-fold cross-validation, the training data is divided into k equal folds. The model is trained k times, each time holding out one fold as validation and training on the remainder. Performance is averaged across folds. Cross-validation reduces the variance of the performance estimate, particularly when data is limited.
The critical rule for test sets: use them once. Every additional evaluation on the test set is mild leakage. In competitive settings, this rule is enforced by keeping the test set labels hidden until submission. In practice, it requires discipline: the developer must commit to a final model before opening the test set.
4. Choosing and Interpreting Evaluation Metrics
Different tasks require different metrics. A single accuracy figure is rarely sufficient.
Classification with imbalanced classes: Accuracy (proportion correct) is misleading when classes are imbalanced. For a binary classifier on data where 95% of examples are class A and 5% are class B, a model that predicts class A for every input achieves 95% accuracy without having learned anything. Appropriate alternatives:
- Precision: of all inputs predicted positive, what fraction were actually positive?
- Recall (sensitivity): of all actual positives, what fraction were predicted positive?
- F1 score: harmonic mean of precision and recall; penalises models that optimise one at the expense of the other.
- AUC-ROC: area under the receiver operating characteristic curve; measures discrimination at all decision thresholds.
Regression: Mean absolute error (MAE) and root mean squared error (RMSE) are standard. RMSE penalises large errors more heavily than MAE, making it appropriate when large errors are disproportionately costly.
Ranking: Normalised discounted cumulative gain (nDCG) measures whether high-relevance items are ranked above lower-relevance items, weighted by position.
Choosing under class imbalance: Report both precision and recall, not only accuracy. State the positive class definition and the class balance. Do not claim a model "performs well" based on accuracy alone without reporting precision and recall.
Statistical significance of performance differences: A difference of 0.5% accuracy between two models may or may not be meaningful, depending on the size of the test set and the variance of the metric. Bootstrap confidence intervals are the standard approach for reporting whether a performance difference is statistically reliable. Report confidence intervals on comparisons of two models, not only point estimates.
Summary
| Concept | Definition |
|---|---|
| Overfitting | Model performs well on training data but poorly on new inputs |
| Data leakage | Test information influences training or evaluation, inflating performance estimates |
| Training leakage | Test data included in training, directly or through shared statistics |
| Temporal leakage | Future data used as features when predicting past events |
| Test set rule | Use the test set once; do not re-tune based on test results |
| Cross-validation | k-fold protocol to reduce variance of performance estimates |
| Precision | Of predicted positives, fraction that are true positives |
| Recall | Of actual positives, fraction that were predicted positive |
| AUC-ROC | Discriminative performance across all classification thresholds |
| Confidence interval on accuracy | Quantifies whether a performance difference between models is statistically reliable |
Practice Tasks
The following tasks have deterministic grading criteria. Complete each before reviewing the answer key.
F7-QS-08-1: Leakage identification
A developer builds a fraud detection classifier. The dataset is split 80/20 into training and test sets after all features are computed. One of the features is "mean transaction amount in the same account in the 30 days following this transaction."
Your task: Identify whether this setup contains data leakage. Name the type of leakage. Explain why it inflates the reported accuracy on the test set. State what the correct feature definition should be.
Grading criteria: This is temporal leakage and feature leakage. "Mean transaction amount in the 30 days following this transaction" uses information that is causally downstream and temporally later than the prediction point — the model would need to see the future to compute this feature at deployment time. The inflation occurs because the feature encodes information about what will happen after the transaction, making fraud events easier to identify than they would be in real deployment. The correct definition is "mean transaction amount in the 30 days preceding this transaction." Responses that identify the leakage, name it as temporal or feature leakage, and correctly state the direction of bias (inflated accuracy) pass.
F7-QS-08-2: Metric selection
A classifier is deployed to detect rare equipment failures in a manufacturing line. Of 10,000 items tested per day, on average 20 fail. A report states the classifier achieves 99.8% accuracy on a representative sample with the same 1:500 failure rate as production. No other metrics are reported.
Your task: Explain whether the 99.8% accuracy figure is sufficient to conclude the classifier is effective at detecting failures. Name the information that is missing, and identify which metric would most directly reveal the classifier's practical utility.
Grading criteria: The 99.8% accuracy does not distinguish a useful classifier from a classifier that predicts "no failure" for every item, which achieves 9,980/10,000 = 99.8% accuracy by default. The missing information is precision and recall. Recall (sensitivity) is the critical metric: what fraction of the 20 actual daily failures does the classifier detect? AUC-ROC would also reveal discriminative performance. Responses that correctly identify that the 1:500 imbalance makes accuracy uninformative, that a trivial classifier achieves the same figure, and that recall or AUC-ROC is the appropriate missing metric, pass.
F7-QS-08-3: Test set discipline
An agent tunes a text classification model. It evaluates on its fixed test set after each training run, observing:
| Run | Validation accuracy | Test accuracy |
|---|---|---|
| 1 | 82% | 83% |
| 2 | 84% | 85% |
| 3 | 86% | 86% |
| 4 | 87% | 85% |
| 5 | 88% | 84% |
The agent selects Run 5 (highest validation accuracy) and reports 84% test accuracy as the final result.
Your task: Identify the methodological error in this evaluation procedure. State what the reported 84% represents and why it is likely an overestimate. Describe the correct procedure.
Grading criteria: The error is using the test set five times — once per run. Even though the final selection is based on validation accuracy, computing test accuracy for every run creates mild contamination: the reported 84% is likely an overestimate of true generalisation performance. The correct procedure is to select the final model based on validation accuracy alone, then evaluate on the test set exactly once after that decision is final. Responses that identify that the test set was used five times, explain that this causes mild contamination and overestimation, and describe a one-shot test evaluation protocol, pass.
Reflective Task (manual scoring)
Prompt:
Identify a machine learning or data-driven evaluation you have encountered, designed, or can credibly construct from an operational context. Describe the evaluation setup in detail, then audit it against the three failure modes covered in this module: overfitting risk, data leakage, and metric appropriateness.
Your account should cover:
- The task and the dataset — what the model predicts and what the training, validation, and test sets represent
- Whether any form of data leakage is present or at risk — temporal, feature, or training leakage — and what the effect on reported performance would be if leakage is present
- Whether the evaluation metric is appropriate for the task type and class balance, and what metric you would add or substitute if not
- Whether the test set was used correctly (once, at the end) or whether re-evaluation occurred, and what effect re-evaluation may have had
- A revised evaluation protocol — what you would change to make the reported performance trustworthy
Minimum length: 300 words. Maximum: 800 words.
Scoring dimensions (for human reviewer):
- Leakage audit (at least one of the three leakage types is correctly assessed with explanation of directional effect): 0–2
- Metric appropriateness (metric choice assessed against task type and class balance, alternative proposed if needed): 0–2
- Test set discipline (usage assessed, correct one-shot protocol described): 0–2
- Revised protocol (at least two concrete changes proposed): 0–2
- Total: 8 points
Canonical answers for deterministic tasks and scoring guidance for reflective tasks are in faculty-07-quantitative-08-model-evaluation-and-generalisation-discipline-answers.md.
Proceed to formal baseline assessment after completing the practice tasks.
Evidence and source notes
- Overfitting and generalisation: Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. Chapter 7 (Model Assessment and Selection) provides the theoretical framework for bias-variance tradeoff and generalisation.
- Data leakage: Kaufman, S., Rosset, S., Perlich, C., & Stitelman, O. (2012). Leakage in data mining: Formulation, detection, and avoidance. ACM Transactions on Knowledge Discovery from Data, 6(4), Article 15. (Formal taxonomy of data leakage types with empirical examples.)
- Temporal leakage in financial forecasting: Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. Chapter 7 (Cross-Validation in Finance) describes temporal leakage and purged k-fold cross-validation as a remedy.
- Classification metrics for imbalanced datasets: Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLOS ONE, 10(3), e0118432.
Version history
| Version | Date | Change |
|---|---|---|
| v0.1.0 | 2026-05-03 | Initial publication. |
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