机器学习-数据泄露

最新推荐文章于 2025-03-15 14:09:27 发布

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最新推荐文章于 2025-03-15 14:09:27 发布

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文章标签：人工智能

原文链接：http://www.cnblogs.com/wdmx/p/9900963.html

本文讨论了在使用scikit-learn进行机器学习前，如何对包含不同类型特征（如文本、浮点数和日期）的数据集进行预处理。重点介绍了在数据预处理阶段可能遇到的问题，如数据泄露导致交叉验证分数不可靠，以及如何将预处理器参数纳入参数搜索。

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Many datasets contain features of different types, say text, floats, and dates, where each type of feature requires separate preprocessing or feature extraction steps. Often it is easiest to preprocess data before applying scikit-learn methods, for example using pandas. Processing your data before passing it to scikit-learn might be problematic for one of the following reasons:

Incorporating statistics from test data into the preprocessors makes cross-validation scores unreliable (known as data leakage), for example in the case of scalers or imputing missing values.
You may want to include the parameters of the preprocessors in a parameter search.

转载于:https://www.cnblogs.com/wdmx/p/9900963.html