ML 训练数据集的本地数据清洗：去重、标准化和质量评分
如何在本地清洗 ML 训练数据集——涵盖使用 MinHash 去重、文本标准化、PII 脱敏和无需云 API 的质量评分。
本指南详细介绍了如何在本地清洗 ML 训练数据集，涵盖使用 MinHash 进行去重、文本标准化、PII 脱敏以及无需依赖云 API 的质量评分。对于受监管行业和处理敏感数据的组织，本地数据清洗是确保数据安全和合规的关键步骤。
数据清洗是 ML 管道中最关键但最容易被忽视的阶段。糟糕的训练数据会直接导致糟糕的模型性能，无论模型架构或训练方法多么先进。本指南提供了实用的技术方法和工具推荐，帮助团队在本地环境中建立高效的数据清洗工作流。
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