KNN如何对手写数字进行识别(python)

from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_digits
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
#加载数据
digits = load_digits()
data = digits.data
#数据探索
print(‘数据形式：‘,data.shape)
#查看第一幅图像
print(‘查看第一幅图像:‘,digits.images[0])
#第一幅图像代表的数字含义
print(‘第一幅图像代表的数字含义:‘,digits.target[0])
#将第一幅图像显示出来
plt.gray()
plt.imshow(digits.images[0])
plt.show()
#分割数据，将25%的数据作为测试集，其余作为训练集
train_x,test_x,train_y,test_y = train_test_split(data,digits.target,test_size=0.25,random_state=33)
#采用Z-Score规范化
ss = preprocessing.StandardScaler()
train_ss_x = ss.fit_transform(train_x)
test_ss_x = ss.transform(test_x)
#创建KNN分类器
knn = KNeighborsClassifier()
knn.fit(train_ss_x,train_y)
predict_y = knn.predict(test_ss_x)
print("KNN准确率：%.4lf"% accuracy_score(predict_y,test_y))
#创建SVM分类器
svm = SVC()
svm.fit(train_ss_x,train_y)
predict_y = svm.predict(test_ss_x)
print(‘SVM准确率：%0.4lf‘% accuracy_score(predict_y,test_y))
#采用Min-Max规范化
mm = preprocessing.MinMaxScaler()
train_mm_x = mm.fit_transform(train_x)
test_mm_x = mm.transform(test_x)
#创建Naive Bayes分类器
mnb = MultinomialNB()
mnb.fit(train_mm_x,train_y)
predict_y = mnb.predict(test_mm_x)
print("多项式朴素贝叶斯准确率：%.4lf" % accuracy_score(predict_y,test_y))
#创建CART决策树分类器
dtc = DecisionTreeClassifier()
dtc.fit(train_mm_x,train_y)
predict_y = dtc.predict(test_mm_x)
print("CART决策树准确率：%.4lf"% accuracy_score(predict_y,test_y))

数据形式： (1797, 64)
查看第一幅图像: [[ 0.  0.  5. 13.  9.  1.  0.  0.]
 [ 0.  0. 13. 15. 10. 15.  5.  0.]
 [ 0.  3. 15.  2.  0. 11.  8.  0.]
 [ 0.  4. 12.  0.  0.  8.  8.  0.]
 [ 0.  5.  8.  0.  0.  9.  8.  0.]
 [ 0.  4. 11.  0.  1. 12.  7.  0.]
 [ 0.  2. 14.  5. 10. 12.  0.  0.]
 [ 0.  0.  6. 13. 10.  0.  0.  0.]]
第一幅图像代表的数字含义: 0

KNN准确率：0.9756
SVM准确率：0.9867
多项式朴素贝叶斯准确率：0.8844
CART决策树准确率：0.8533