KNN手写数字数据集_knn.zip

# -*- coding: utf-8 -*- # 手写数字分类 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(data.shape) # 查看第一幅图像 print(digits.images[0]) # 第一幅图像代表的数字含义 print(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(test_y, predict_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(test_y, predict_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(test_y, predict_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(test_y, predict_y))