scikits 机器学习中的缺失值 [英] Missing values in scikits machine learning

问题描述

scikit-learn 中是否可能存在缺失值?他们应该如何代表?我找不到任何关于此的文档.

Is it possible to have missing values in scikit-learn ? How should they be represented? I couldn't find any documentation about that.

推荐答案

scikit-learn 根本不支持缺失值.之前在邮件列表上已经讨论过这个问题，但没有尝试实际编写代码来处理它们.

无论你做什么，不要使用 NaN 来编码缺失值，因为许多算法拒绝处理包含 NaN 的样本.

Whatever you do, don't use NaN to encode missing values, since many of the algorithms refuse to handle samples containing NaNs.

以上答案已经过时；scikit-learn 的最新版本有一个类 Imputer 做简单的，每个特征的缺失值插补.您可以向它提供包含 NaN 的数组，以将其替换为相应特征的均值、中值或众数.

The above answer is outdated; the latest release of scikit-learn has a class Imputer that does simple, per-feature missing value imputation. You can feed it arrays containing NaNs to have those replaced by the mean, median or mode of the corresponding feature.

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