Unverified50% confidenceFactExact time
Target leakage occurs when a feature contains information about the target that should not legitimately be available at prediction time, or when information about the target or the evaluation process enters training in an invalid way, leading to misleading model evaluation.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
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Related Claims
UnverifiedIn Agentic scenarios, sources of error may include prompt design flaws, tool API return formats not matching expectations, information loss during multi-step reasoning, or gaps in the model's domain knowledge.78% similarVerifiedData leakage refers to test set information being indirectly seen by the model during training, causing evaluation metrics to be inflated; it is one of the most common and most concealed methodological flaws in machine learning research, and is especially dangerous in the medical prediction domain.77% similarVerifiedData leakage refers to future information or target-related information that should not appear in training data accidentally flowing into the feature set, commonly seen in time series misuse of future features or normalization performed before splitting.76% similarUnverifiedInference methods based on model internal features face a trade-off between false positives and false negatives76% similarUnverified数据泄漏是因意外引入测试集信息而导致模型评估虚高的陷阱，常见形式包括在划分数据集之前就对全量数据做标准化或使用时间上因果倒置的特征74% similar
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