我试图在python中执行10倍交叉验证。我知道如何计算混淆矩阵和分裂测试报告(例如分开80%训练和20%测试)。但问题是我不知道如何计算混淆矩阵和每个折叠的报告,例如fold-10时,我只知道平均准确度的代码。
这是一个可重复的例子,其中包含乳腺癌数据和简单的3倍CV:
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import KFold
X, y = load_breast_cancer(return_X_y=True)
n_splits = 3
kf = KFold(n_splits=n_splits, shuffle=True)
model = DecisionTreeClassifier()
for train_index, val_index in kf.split(X):
model.fit(X[train_index], y[train_index])
pred = model.predict(X[val_index])
print(confusion_matrix(y[val_index], pred))
print(classification_report(y[val_index], pred))
结果是3个混淆矩阵和分类报告,每个CV折叠一个:
[[ 63 9]
[ 10 108]]
precision recall f1-score support
0 0.86 0.88 0.87 72
1 0.92 0.92 0.92 118
micro avg 0.90 0.90 0.90 190
macro avg 0.89 0.90 0.89 190
weighted avg 0.90 0.90 0.90 190
[[ 66 8]
[ 6 110]]
precision recall f1-score support
0 0.92 0.89 0.90 74
1 0.93 0.95 0.94 116
micro avg 0.93 0.93 0.93 190
macro avg 0.92 0.92 0.92 190
weighted avg 0.93 0.93 0.93 190
[[ 59 7]
[ 8 115]]
precision recall f1-score support
0 0.88 0.89 0.89 66
1 0.94 0.93 0.94 123
micro avg 0.92 0.92 0.92 189
macro avg 0.91 0.91 0.91 189
weighted avg 0.92 0.92 0.92 189