机器学习算法的分类准确度

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split


digits = datasets.load_digits()

x = digits.data #获取特征值
y = digits.target #获取标记

#将数据分为两部分，训练数据和测试数据

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)

#指定key值
knn = KNeighborsClassifier(n_neighbors=3)

#进行拟合
knn.fit(x_train,y_train)

y_predict = knn.predict(x_test)

ratio = sum(y_predict==y_test)/len(y_test)

print(ratio)

#当我们不想要预测值的时候，我们可以直接使用knn对象的score函数进行得出准确度
ratio_bak = knn.score(x_test,y_test)
print(ratio_bak)