January 2018
Intermediate to advanced
301 pages
8h 54m
Chinese
Content preview from Python机器学习基础教程
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230
｜
第
5
章
我们得到等于
1.0
的完美
AUC
。这意味着根据决策函数，所有正类点的排名要高于所有反
类点。换句话说，利用正确的阈值，这个模型可以对所有数据进行完美分类！
10
知道这一
点，我们可以调节这个模型的阈值并得到很好的预测结果。如果我们仅使用精度，那么将
永远不会发现这一点。
因此，我们强烈建议在不平衡数据上评估模型时使用
AUC
。但请记住，
AUC
没有使用默
认阈值，因此，为了从高
AUC
的模型中得到有用的分类结果，可能还需要调节决策阈值。
5.3.3
多分类指标
前面我们已经深入讨论了二分类任务的评估，下面来看一下对多分类问题的评估指标。多
分类问题的所有指标基本上都来自于二分类指标，但是要对所有类别进行平均。多分类的
精度被定义为正确分类的样本所占的比例。同样，如果类别是不平衡的，精度并不是很
好的评估度量。想象一个三分类问题，其中
85%
的数据点属于类别
A
，
10%
属于类别
B
，
5%
属于类别
C
。在这个数据集上
85%
的精度说明了什么？一般来说，多分类结果比二分
类结果更加难以理解。除了精度，常用的工具有混淆矩阵和分类报告，我们在上一节二分
类的例子中都见过。下面我们将这两种详细的评估方法应用于对 digits 数据集中
10
种不
同的手写数字进行分类的任务：
In[63]:
from sklearn.metrics import accuracy_score
X_train, X_test, y_train, y_test = train_test_split(
digits.data, digits.target, ...
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