數(shù)據(jù)集使用了Keras的datasets

from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
from keras.datasets import mnist(x_train_tmp, y_train_tmp), (x_test_tmp, y_test_tmp) = mnist.load_data()
x_test_new = x_test_tmp.reshape(x_test_tmp.shape[0], -1)
total_num = x_test_new.shape[0]
test_num = int(total_num / 10)
x_test = x_test_new[:test_num]
y_test = y_test_tmp[:test_num]
x_train = x_test_new[test_num:]
y_train = y_test_tmp[test_num:]classifier = KNeighborsClassifier(n_neighbors=5)
classifier.fit(x_train, y_train)y_pred = classifier.predict(x_test)
print(classification_report(y_test, y_pred))

打印結(jié)果如下：

              precision    recall  f1-score   support0       0.94      0.99      0.97        851       0.91      1.00      0.95       1262       0.97      0.91      0.94       1163       0.93      0.93      0.93       1074       0.91      0.91      0.91       1105       0.95      0.92      0.94        876       0.97      0.97      0.97        877       0.89      0.91      0.90        998       0.95      0.83      0.89        899       0.86      0.87      0.87        94accuracy                           0.93      1000macro avg       0.93      0.92      0.93      1000
weighted avg       0.93      0.93      0.93      1000

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總結(jié)

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