https://terms-en.ai-term-hub.com/zh/tags/data-integrity/Recent content in Data Integrity on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000https://terms-en.ai-term-hub.com/zh/terms/leakage/Sat, 18 Jul 2026 11:23:53 +0000https://terms-en.ai-term-hub.com/zh/terms/leakage/<h2 id="definition">Definition</h2> <p>数据泄露是机器学习中的一个关键错误，指模型在训练过程中获取了在预测时无法获得的信息。这通常是由于不恰当的数据处理（如未正确划分训练集和测试集）造成的。</p> <h3 id="summary">Summary</h3> <p>当训练数据集之外的信息无意中影响模型时，就会发生数据泄露，导致性能评估过于乐观。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>目标泄露</li> <li>训练-测试污染</li> <li>正确的数据分割</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>调试模型过拟合问题</li> <li>验证特征工程流程</li> <li>确保模型评估的稳健性</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/cross-validation-%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81/">Cross-validation (交叉验证)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/feature-engineering-%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B/">Feature engineering (特征工程)</a></li> </ul>