我尝试在标准数据集”iris.csv”上进行预测
然后我得到了一个ValueError错误
File "C:/Users/Ultra/PycharmProjects/poker_ML/decision_tree.py", line 20, in <module> average_precision = average_precision_score(testY, y_score) File "C:\Users\Ultra\PycharmProjects\poker_ML\venv\lib\site-packages\sklearn\metrics\ranking.py", line 241, in average_precision_score average, sample_weight=sample_weight) File "C:\Users\Ultra\PycharmProjects\poker_ML\venv\lib\site-packages\sklearn\metrics\base.py", line 74, in _average_binary_score raise ValueError("{0} format is not supported".format(y_type))ValueError: multiclass format is not supported我如何为3个类别计算精确率-召回率？scikit-learn中的决策树如何计算精确率-召回率？我在计算”y_score”时可能有错误吗？
回答：
根据scikit-learn文档，average_precision_score无法处理多类分类问题。
相反，您可以使用precision_score，如下所示：
# Decision tree...y_pred = decision.predict(testX)y_score = decision.score(testX, testY)print('Accuracy: ', y_score)# Compute the average precision scorefrom sklearn.metrics import precision_scoremicro_precision = precision_score(y_pred, testY, average='micro')print('Micro-averaged precision score: {0:0.2f}'.format( micro_precision))macro_precision = precision_score(y_pred, testY, average='macro')print('Macro-averaged precision score: {0:0.2f}'.format( macro_precision))per_class_precision = precision_score(y_pred, testY, average=None)print('Per-class precision score:', per_class_precision)请注意，您需要指定如何平均得分。这在数据集显示标签不平衡时尤为重要（尽管iris数据集没有这种情况）。
