Tag
data-leakage
- Oct 8, 2026 Point-in-Time Correctness in Training Set Assembly Joins between labels and features must respect event time, not ingestion time; how to build a training table that never sees the future. machine learning
- Sep 10, 2026 Why a Held-Out Test Set Cannot Be Reused What a test-set score actually estimates, how a second look at the same rows biases it upward, and the leakage paths that corrupt the number before any model is fitted. machine learning
- 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
- Aug 12, 2023 Point-in-Time Feature Reads and the Skew They Prevent Training a model on features computed after the fact and serving it on features computed at request time yields two different distributions; point-in-time joins are the mechanism that forces them to agree. inference systems
- 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