Data Contamination — When Evaluation Data Leaks into Training and Performance Looks Better Than It Is

Data contamination happens when test or validation data accidentally ends up in the training data, causing a model’s reported performance to be overestimated. The model didn’t truly get better at solving unseen problems—it’s effectively being graded on content it has already encountered.

02/09/2026

Group K-Fold — Group-aware cross-validation to prevent data leakage

Group K-Fold is a cross-validation strategy that splits data so that samples from the same group (e.g., a user·patient·device·or customer) never appear in both the training set and the validation set at the same time. This prevents data leakage, where a model looks artificially strong simply because it has effectively “seen” the same entity during training.

02/15/2026

Information Leakage — When Models Learn From Information They Should Never See

Information Leakage occurs when information that would not be available at prediction time accidentally enters the feature set, preprocessing pipeline, data split, or evaluation process, causing a model’s performance to look better than it really is. A model affected by leakage may achieve excellent accuracy during development, but that performance often disappears after deployment because it depended on information that will never exist in a real prediction environment.

08/02/2026

Stacking Ensemble — An Ensemble Method That Learns from Multiple Model Predictions to Make the Final Prediction

Stacking Ensemble is an ensemble method in which multiple base models first make predictions, and then a meta model takes those predictions as input to produce the final prediction. The key idea is that, instead of using a simple average or majority vote, stacking learns which model should be trusted more under different conditions.

04/04/2026

Target Encoding — Why Can Target Statistics Improve Accuracy While Increasing Overfitting Risk?

Target Encoding is an encoding technique that transforms a categorical feature into statistics derived from the target variable, instead of assigning arbitrary numeric IDs or creating one-hot vectors. Each category is replaced with the average target value observed for that category, allowing the model to learn the relationship between categories and outcomes more directly. This approach is particularly effective for high-cardinality categorical features because it reduces dimensionality while preserving predictive information. However, since it relies on target statistics, it also introduces a greater risk of overfitting and Data Leakage if applied incorrectly.

08/08/2026

Target Leakage — Why Great Training Performance Can Collapse in the Real World

Target Leakage happens when a model is trained with information that would not actually be available at the moment a prediction must be made. Because those features provide hidden clues about the target, the model can achieve impressive results during training or evaluation while performing far worse once it is deployed on real-world data.

08/15/2026