scikit-learn:打印分类报告，求准确率、精确率、召回率、F1值等指标

一、打印分类报告（使用scikit-learn库中的函数）

from sklearn.metrics import classification_report# y_test为测试集真实标签， y_pred为测试集预测的标签
print(classification_report(y_test, y_pred))

例子：

# Standard scientific Python imports
import matplotlib.pyplot as plt# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, metrics
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
from sklearn.model_selection import train_test_split# The digit dataset
digits = datasets.load_digits()"""The data that we are interested in is made of 8x8 images of digits, Let'shave a look at the first 4 images, stored in the 'images' attributes of the dataset. If we were working from image files, we could load them using'matplotlib.pyplot.imread'. Note that each image must have the same size. For these images, we know which digit they represent: it is given in the 'target' of the dataset.
"""_, axes = plt.subplots(2, 4)
images_and_labels = list(zip(digits.images, digits.target))
for ax, (image, label) in zip(axes[0, :], images_and_labels[:4]):ax.set_axis_off()ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')ax.set_title('Training: %d' % label)# To apply a classifier on this data, we need to flatten the image,
# to turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))classifier = svm.SVC(gamma=0.001)# Split data into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(data, digits.target, test_size=0.5, shuffle=False)# We learn the digits on the first half of the digits
classifier.fit(X_train, y_train)# Now predict the value of the digit on the second half:
predicted = classifier.predict(X_test)images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
for ax, (image, prediction) in zip(axes[1, ], images_and_predictions[:4]):ax.set_axis_off()ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')ax.set_title('Prediction: %i' % prediction)print('Classification report for classifier %s:\n%s\n' % (classifier, metrics.classification_report(y_test, predicted)))
cm = confusion_matrix(y_test, predicted, labels=digits.target_names)
print(cm)
disp = ConfusionMatrixDisplay(cm, display_labels=digits.target_names)
disp.plot()
plt.show()

output:

Classification report for classifier SVC(gamma=0.001):precision    recall  f1-score   support0       1.00      0.99      0.99        881       0.99      0.97      0.98        912       0.99      0.99      0.99        863       0.98      0.87      0.92        914       0.99      0.96      0.97        925       0.95      0.97      0.96        916       0.99      0.99      0.99        917       0.96      0.99      0.97        898       0.94      1.00      0.97        889       0.93      0.98      0.95        92accuracy                           0.97       899macro avg       0.97      0.97      0.97       899
weighted avg       0.97      0.97      0.97       899

参考链接：
https://scikit-learn.org.cn/view/45.html

二、求准确率，精确率，召回率、F1值等。

from sklearn.metrics import accuracy_score, recall_score, f1_score, precision_score
y_test = [0, 3, 4, 12, 5, 5, 4, 1, 2]
y_pred = [0, 1, 2, 2, 3, 3, 3, 2, 2]print('Accuracy score:', accuracy_score(y_test, y_pred))
print('Recall:', recall_score(y_test, y_pred, average='weighted'))
print('F1-score:', f1_score(y_test, y_pred, average='weighted'))
print('Precision score:', precision_score(y_test, y_pred, average='weighted'))

out:

Accuracy score:0.2222222222222222
Recall: 0.2222222222222222
F1-score: 0.15555555555555556
Precision score: 0.1388888888888889

也可以用precision_recall_fscore_support(y_true, y_pred, average='weighted'),跟上面是一样的效果。
参考链接：

https://scikit-learn.org/stable/modules/model_evaluation.html#
https://github.com/wmn7/Traffic-Classification/blob/master/TrafficFlowClassification/utils/evaluate_tools.py

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