Tag
cross-validation
- Jun 18, 2026 How a Feature Becomes a Leak Leakage defined as a dependency between the training representation and the evaluation labels, the three mechanisms that create it, and why it inflates validation without touching training error. machine learning
- Oct 13, 2025 Transformer State and the Fold Boundary Why every fitted preprocessing object must live inside the cross-validation loop, which statistics actually matter, and how refitting at the end reintroduces the leak. machine learning
- Sep 26, 2025 Group and Duplicate Leakage in Cross-Validation Splits Why row-level splitting fails when rows are not independent, how grouped and blocked folds restore the estimate, and what the fold count costs in group imbalance. machine learning
- Oct 10, 2024 Out-of-Fold Target Encoding for High-Cardinality Categoricals How smoothing trades bias for variance in target encoding, why the encoding must be computed out of fold, and what the left-out fold estimate costs in noise. machine learning
- Mar 5, 2024 Offline Evaluation Leakage and the Metrics That Hide It How random splits, duplicated entities, and temporal leakage inflate offline metrics, and which split design gives an estimate that predicts deployment. machine learning
- Sep 15, 2023 Nested Cross-Validation and the Optimism of Tuned Scores Why a cross-validated score from a tuned procedure is biased, how the inner and outer loops separate selection from estimation, and what the procedure costs. machine learning
- Jan 10, 2023 Stratified k-Fold Cross-Validation and the Variance of the Estimate How k-fold partitioning produces a spread estimate, why the folds are not independent, and what stratification actually removes from the variance. machine learning
- Jan 5, 2023 Blocked and Rolling-Origin Validation for Time-Ordered Data Why random folds fail on time series, how expanding and sliding origins differ in what they measure, and where the gap between folds must be inserted. machine learning
- May 12, 2022 Grouped Splits When Observations Are Not Independent How shared subjects across rows inflate a random split, what group-aware partitioning guarantees, and how to choose the grouping key when several exist. machine learning