[英]Difference between score and accuracy_score in sklearn
Whats the difference between score()
method in sklearn.naive_bayes.GaussianNB()
module and accuracy_score
method in sklearn.metrics
module? sklearn.naive_bayes.GaussianNB()
模块中的score()
方法和sklearn.naive_bayes.GaussianNB()
模块中的accuracy_score
方法有什么sklearn.metrics
? Both appears to be same.两者似乎是一样的。 Is that correct?
那是正确的吗?
In general, different models have score methods that return different metrics.通常,不同的模型具有返回不同指标的评分方法。 This is to allow classifiers to specify what scoring metric they think is most appropriate for them (thus, for example, a least-squares regression classifier would have a
score
method that returns something like the sum of squared errors).这是为了允许分类器指定他们认为最适合他们的评分指标(例如,最小二乘回归分类器将有一个
score
方法,该方法返回类似于平方误差总和的内容)。 In the case of GaussianNB
the docs say that its score method:在
GaussianNB
的情况下,文档说它的评分方法:
Returns the mean accuracy on the given test data and labels.
返回给定测试数据和标签的平均准确度。
The accuracy_score
method says its return value depends on the setting for the normalize
parameter: accuracy_score
方法说它的返回值取决于normalize
参数的设置:
If False, return the number of correctly classified samples.
如果为 False,则返回正确分类的样本数。 Otherwise, return the fraction of correctly classified samples.
否则,返回正确分类样本的分数。
So it would appear to me that if you set normalize
to True
you'd get the same value as the GaussianNB.score
method.所以在我看来,如果您将
normalize
设置为True
您将获得与GaussianNB.score
方法相同的值。
One easy way to confirm my guess is to build a classifier and call both score
with normalize = True
and accuracy_score
and see if they match.确认我的猜测的一种简单方法是构建一个分类器并使用
normalize = True
和accuracy_score
调用这两个score
并查看它们是否匹配。 Do they?他们吗?
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