scikit 中的大多数分类器都有一个内置的 score() 函数，您可以在其中输入 X_test 和 y_test，它将输出适当的度量那个估计.对于分类估计器，它主要是'平均准确度'.

Most classifiers in scikit have an inbuilt score() function, in which you can input your X_test and y_test and it will output the appropriate metric for that estimator. For classification estimators it is mostly 'mean accuracy'.

还有 sklearn.metrics 有许多可用的函数可以输出不同的指标，例如 accuracy、precision、recall 等.

对于您的具体问题，您需要accuracy_score

For your specific question you need accuracy_score

from sklearn.metrics import accuracy_score
score = accuracy_score(iris.target, pr)