Data Leakage
Data Leakage is a training error that occurs when information from outside the training dataset is used to train a model. This leads to overly optimistic performance scores during validation, but poor generalization on true unseen data.
Frequently Asked Questions
How does data leakage typically happen?▼
By scaling or normalizing the entire dataset (including training and validation sets) together, rather than calculating scaling parameters only on the training split.
How do you prevent data leakage?▼
Strictly separate the validation and test datasets before performing any preprocessing, cleaning, or feature engineering transformations.
Quick Facts
- CategoryModel Training
- Key ApplicationDataset splitting audits, feature engineering checks, and cross-validation pipelines.
Coverage Trend12 Weeks
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