如何在决策树 sklearn 中计算精确召回？

Question

我尝试在标准数据集“iris.csv”中进行预测import pandas as pdfrom sklearn import treedf = pd.read_csv('iris.csv')df.columns = ['X1', 'X2', 'X3', 'X4', 'Y']df.head()# Decision treefrom sklearn.model_selection import train_test_splitdecision = tree.DecisionTreeClassifier(criterion='gini')X = df.values[:, 0:4]Y = df.values[:, 4]trainX, testX, trainY, testY = train_test_split(X, Y, test_size=0.25)decision.fit(trainX, trainY)y_score = decision.score(testX, testY)print('Accuracy: ', y_score)# Compute the average precision scorefrom sklearn.metrics import average_precision_scoreaverage_precision = average_precision_score(testY, y_score)print('Average precision-recall score: {0:0.2f}'.format(      average_precision))我有 valueerrorFile "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如何计算 3 类的精确召回率？sklearn 中决策树的精确召回是如何工作的。也许我在计算“y_score”时有错误？

Answer 1

根据scikit-learn 文档 average_precision_score无法处理多类分类。

相反，您可以precision_score像这样使用：

# Decision tree

...

y_pred = decision.predict(testX)

y_score = decision.score(testX, testY)

print('Accuracy: ', y_score)

# Compute the average precision score

from sklearn.metrics import precision_score

micro_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分数。如果您的数据集显示标签不平衡（iris事实并非如此），这一点尤其重要。