Audit your ML workflow for the many forms of data leakage that inflate scores and collapse in production.
## CONTEXT Data leakage is the silent killer of ML projects: a model scores 0.99 in validation, ships, and fails, because information from the future or the target sneaked into training. Leakage hides in preprocessing fit before splitting, target-derived features, temporal lookahead, duplicate rows across splits, and…
Premium Prompt
Unlock this prompt — and all 25,000+ expert-crafted prompts — with Pro.
Unlock with Pro