August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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文本分类算法
｜
191
accuracy 0.87 9006
macro avg 0.40 0.24 0.26 9006
weighted avg 0.81 0.87 0.83 9006
根据我们的计算和前面的分类报告，有一个问题非常明显：
P3
类别的召回率和精确
率非常高，而其他类别的这些值却很低，在某些情况下（
P5
）甚至为
0
。该模型的
整体准确率为
88%
，但即使永远预测
P3
，准确率也能达到
88%
。这表明，我们的模
型并没有学习到太多东西，只不过是在预测时选择了多数类而已。这充分说明了，
在模型评估期间，我们必须分析多个指标，而不能仅依靠准确性。
类别不平衡
导致模型这种行为的原因是
priority
中的类别不平衡（我们前面也观察到了）。
priority
为
P3
的错误有将近
36000
个，但其他类别的错误数量只有
4000
个，有些类
别甚至更少。这意味着，当我们训练模型时，它只能学习到
P3
类别的特征。
为了克服类别不平衡的问题，我们可以采取几种技巧。这些技巧主要分为上采样和
下采样两种。上采样指的是，人为地增加少数类（我们示例中所有非
P3
类别）的
观测数量。这些方法多种多样，有些只是简单地添加多个复制样本，而有些则是采
用
SMOTE
之类的方法生成新观测。
注
1
下采样指的是减少多数类（我们示例中的
P3
类别）的观测数量。我们选择针对
P3
类别进行随机下采样，将
P3
的观测数量降
至与其他类别接近的水平： ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.