Leakage-Resistant Evaluation Pipelines

• Leakage-resistant evaluation pipelines are structured methodologies that enforce strict separation between training and evaluation data to prevent unintentional information flow.
• They apply rigorous protocols such as correct chronological splitting, per-fold transformations, and static code analysis to mitigate bias in performance metrics.
• These pipelines are crucial in diverse applications, including time series forecasting, biomedical harmonization, and large language models, reducing inflated accuracy by up to 20%.

Leakage-resistant evaluation pipelines are structured methodologies for training, validation, and assessment in machine learning systems that rigorously prevent the unintentional flow of information from evaluation and test sets into the training process. Their importance has grown in response to consistent findings that even subtle missteps in data partitioning, preprocessing, or model selection can generate highly optimistic biases, invalidate performance metrics, and undermine the reliability of scientific claims. Leakage manifests in supervised forecasting, harmonization in biomedical multi-site studies, micro-expression analysis, and the evaluation of LLMs. The following sections detail the key frameworks, mitigation protocols, and empirical benchmarks central to leakage-resistant pipelines.

1. Definitions and Taxonomy of Leakage

Leakage arises whenever information unavailable at deployment time influences any aspect of training, preprocessing, or hyperparameter selection. The formal taxonomy, as detailed in "On Leakage in Machine Learning Pipelines" (Sasse et al., 2023), is summarized below:

Leakage Type	Cause	Pipeline Stage
Test-to-Train	Use of test data in training or param.	Preprocessing, Training
Test-to-Test	Statistical coupling of test samples	Test-set preprocessing
Feature-to-Target	Features constructed with target info	Feature engineering
Target Leakage	Targets used in transformation/training	Preprocessing, Feature eng.
Dataset Leakage	Adaptive/exhaustive reuse of benchmarks	Research lifecycle
Confound Leakage	Inadequate confound regression	Preprocessing

Leakage is not restricted to supervised learning; it extends to harmonization (site/target confounds), multi-modal learning, and even validation routines. The root causes commonly involve violations of data split invariants (e.g., fitting transforms before partitioning) and improper use of external labels. Each type of leakage has distinct consequences for generalization, statistical significance, and claim validity.

2. Partitioning Strategies and Sequence Generation

In time series forecasting, particularly for LSTM architectures, sequence generation methodology is a major determinant of leakage risk. "Hidden Leaks in Time Series Forecasting" (Albelali et al., 7 Dec 2025) contrasts two approaches:

• Leaky sequence generation ("pre-split"): Sliding windows are created from the entire series before partitioning. This setup allows windows in training to overlap with future (i.e., to-be-validation/test) points, inflating apparent accuracy.
• Clean sequence generation ("post-split"): The series is split into strict chronological train/val/test partitions; windowed input–output pairs are then generated independently within each partition, maintaining total temporal causality.

Empirical results show severe vulnerability in 10-fold cross-validation. RMSE Gain, defined as

$$\text{RMSE Gain (\%)} = 100\% \times \frac{\mathrm{RMSE}_{\text{leaky}} - \mathrm{RMSE}_{\text{clean}}}{\mathrm{RMSE}_{\text{clean}}},$$

peaks at 20.5% for 10-fold CV at lag $h=3$, while 2-way and 3-way splits remain under 5% in all tested regimes. Smaller window sizes and longer forecasting horizons exacerbate leakage. Increasing window size $W$ systematically reduces sensitivity in all validation designs.

Recommended sequence generation for leakage resistance:

1. Chronologically split raw data into train/val/test (or folds).
2. Generate sliding windows within each partition.
3. Apply blocked or forward-chaining CV with non-overlapping boundaries if cross-validation is required.

3. Preprocessing, Harmonization, and Multi-Site Protocols

Data harmonization is susceptible to leakage when target labels must be known for correct removal of nuisance variance. In class-imbalanced, multi-site biomedical datasets, the standard ComBat harmonization protocol leaks when labels from test sets are included as covariates in parameter estimation, especially under site–target dependence ("Impact of Leakage on Data Harmonization" (Nieto et al., 25 Oct 2024)). The PrettYharmonize algorithm addresses this with a "pretend-label" approach:

• Estimate harmonization parameters on training data only, including true labels as covariates.
• For each test sample, iterate harmonization under each possible label (or bin); generate predictions for each.
• Stack these predictions as meta-features for a downstream predictor trained on train meta-features and true labels.

PrettYharmonize matches leakage-prone methods in performance under site–target dependence, but does so without any test target leakage. In all independence scenarios, performance difference between leakage-prone and leakage-free harmonization protocols vanishes, confirming that gains in the former likely reflect exploitation of site–target confounds.

4. Automated Detection and Code-Level Safeguards

Static analysis tools have emerged to operationalize leakage detection and prevention in ML codebases. LeakageDetector (AlOmar et al., 18 Mar 2025) uses a Datalog-based rules engine coupled with PyCharm IDE integration to flag, categorize, and suggest fixes for three major classes:

• Overlap leakage: Resampling before partitioning; synthetic instances span multiple sets.
• Multi-test leakage: Reuse of hold-out set for multiple model fits/hyperparameter searches inflates perceived accuracy.
• Preprocessing leakage: Fitting data-driven preprocessors (scalers, selectors) on the full dataset before splitting.

Detection relies on AST-to-facts translation and declarative pattern matching in code. Remedies involve reordering partitioning calls above any sampling/scaling/selection, ensuring each test instance is evaluated strictly once, and encapsulating transformations within split-aware pipelines. Empirical validation reports preprocessing leaks as the dominant pattern.

5. Design Principles and Best Practices

Comprehensive guidelines for leakage-resistant pipeline design have been distilled from multiple empirical and methodological studies (Sasse et al., 2023, Albelali et al., 7 Dec 2025, Nieto et al., 25 Oct 2024). Key principles include:

1. Strict train/test separation: Never fit any transform (scaling, selection, harmonization) on pooled or unsplit data.
2. Per-fold transformations in cross-validation: Learn and apply all preprocessing within each training fold; test data must remain untouched.
3. Nested cross-validation for selection: Hyperparameter/model selection must use nested CV; outer fold held-out error provides the actual generalization metric.
4. Independent test sample processing: Test samples should not "see" other test samples during transformation.
5. Leakage metrics and controls: Report depletion or inflation metrics (RMSE Gain, absolute/relative decrements), especially for new benchmarking pipelines.
6. Transparent code releases and documentation: Provide pipeline code, splits, metric aggregation scripts, and full version records.

Below is a summary of optimal configurations for time series forecasting pipelines, exemplifying the above principles:

Validation Design	RMSE Gain (max)	Recommended Split Order	Leakage Mitigation
2-way split	<5%	Chronological, pre-window	Split first
3-way split	<5%	Chronological	Split first
10-fold CV	Up to 20.5%	Blocked (non-overlap)	Fold-specific

6. Leakage-Resistant Protocols in Specialized Domains

Micro-expression analysis presents unique leakage risks due to limited subject population and data sparsity (Varanka et al., 2022). Protocols enforce subject- and dataset-disjoint splits:

$$S_{\mathrm{train}} \cap S_{\mathrm{val}} = \emptyset, \quad S_{\mathrm{train}} \cap S_{\mathrm{test}} = \emptyset, \quad S_{\mathrm{val}} \cap S_{\mathrm{test}} = \emptyset.$$

Leave-One-Dataset-Out initialization and train/val/test splitting at subject level eliminate cross-fold leaks. Early stopping and feature selection operate only within D_train or D_val, never referencing D_test. Metrics are aggregated globally rather than fold-wise to ensure unbiased performance assessment.

7. Leakage in Benchmark Construction and LLMs

Benchmark leakage in LLMs is frequently detected via comparative atomic metrics such as Perplexity (PPL) and n-gram accuracy (Xu et al., 29 Apr 2024). If PPL for original benchmark examples is significantly lower than for paraphrased/synthesized references, memorization and leakage are likely. The unified pipeline is as follows:

• Generate paraphrased references for both train and test splits.
• Compute PPL and n-gram accuracy for each split, comparing original and reference benchmarks.
• Compute absolute and relative decrements and the train–test difference:

$$\delta_{\text{train,test}} = \delta_{\text{train}} - \delta_{\text{test}}$$

• Filter instances yielding perfect matches in n-gram predictions.
• Report both raw and filtered scores, plus the fraction of examples removed.
• Release a Benchmark Transparency Card documenting overlap and evaluation protocols.

Empirical results show that instances of δ_train_test exceeding 10% reliably indicate substantial leakage or memorization of test benchmarks, particularly in Aquila and Qwen models. Models known not to leak (LLaMA series, Mistral, Grok-1) maintain δ_train_test below 3–5%.

---

Leakage-resistant evaluation pipelines are essential for the actionable interpretation of machine learning research, particularly under conditions of temporal, cohort, site, and benchmark partitioning. Rigorous adoption of split-first protocols, fold-specific transformations, leakage-aware harmonization, static code analysis, and transparent benchmarking practices are central to trustworthy, deployable and reproducible model development.