October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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无监督学习技术
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图 9-18：束状集群（左）和球形集群（右）的高斯混合
9.2.1 使用高斯混合进行异常检测
异常检测（也称为离群值检测）是检测严重偏离标准的实例的任务。这些实例称为异常
或离群值，而正常实例称为内值。异常检测在各种应用中很有用，例如欺诈检测，在制
造业中检测有缺陷的产品，或在训练一个模型之前从数据集中删除异常值（这可以显著
提高所得模型的性能）。
使用高斯混合模型进行异常检测非常简单：位于低密度区域的任何实例都可以被视为异
常。你必须定义要使用的密度阈值。例如，在试图检测缺陷产品的制造业公司中，缺陷
产品的比例通常是已知的，假设它等于 4%。将密度阈值设置为导致 4% 的实例位于该阈
值密度以下的区域中的值。如果你发现误报过多（即标记为有缺陷的好产品），则可以降
低阈值。相反，如果假负过多（即系统未将其标记为次品的次品），则可以提高阈值。这
是通常的精度 / 召回的权衡（见第 3 章）。以下是使用最低 4% 的密度作为阈值来识别异
常值的方法（即大约 4% 的实例被标记为异常）：
densities = gm.score_samples(X)
density_threshold = np.percentile(densities, 4)
anomalies = X[densities < density_threshold]
图 9-19 中将这些异常值表示为星号。
一个与之密切相关的任务是新颖性检测，它与异常检测不同之处在于，该算法被假定为
在“干净”的数据集上训练的，不受异常值的污染 ...
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