sklearn手册5.14.2节 comparison of F-test and mutual information

最新推荐文章于 2025-02-27 10:12:48 发布

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最新推荐文章于 2025-02-27 10:12:48 发布 · 398 阅读

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此示例通过比较单变量F检验和互信息展示了它们在特征选择中的差异。F检验因只捕捉线性依赖，将x_1评为关键特征，而互信息能捕捉任意依赖，认为x_2最重要。两者都识别x_3为不相关特征。

5.14.2 comparison of F-test and mutual information

"This example illustrates the differences between univariate F-test statistics and mutual information.
We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the target depends on them as follows:
y = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the third features is completely irrelevant.
The code below plots the dependency of y against individual x_i and normalized values of univariate F-tests statistics
and mutual information.
As F-test captures only linear dependency, it rates x_1 as the most discriminative feature. On the other hand, mutual
information can capture any kind of dependency between variables and it rates x_2 as the most discriminative feature,
which probably agrees better with our intuitive perception for this example. Both methods correctly marks x_3 as
irrelevant."

"本例展示单变量F检验和互信息之间的差别。假设一个数据集中有三个特征x_1、x_2、x_3，三个特征的值均符合在区间[0,1]上的均匀分布。目标值与它们关系如下：