手写数字识别-传统机器学习

# 1.导包 import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_digits from sklearn.preprocessing import MinMaxScaler, StandardScaler from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.naive_bayes import MultinomialNB from sklearn.ensemble import AdaBoostClassifier from sklearn.metrics import accuracy_score # 2.加载数据并探索 # 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.imshow(digits.images[0]) plt.show() (1797, 64) [[ 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.]] 0 Process finished with exit code 0

# 3.分割数据集并规范化 # 数据及目标 data1 = digits.data target1 = digits.target # # 数据增广 # data2 = np.vstack((data1, data1)) # target2 = np.hstack((target1, target1)) data2 = np.vstack((data1, data1, data1)) target2 = np.hstack((target1, target1, target1)) # 分割数据，将25%的数据作为测试集，其余作为训练集（你也可以指定其他比例的数据作为训练集） # train_x, test_x, train_y, test_y = train_test_split(data1, target1, test_size=0.25) train_x, test_x, train_y, test_y = train_test_split(data2, target2, test_size=0.25) # 采用z-score规范化 ss = StandardScaler() train_ss_scaled = ss.fit_transform(train_x) test_ss_scaled = ss.transform(test_x) # 采用0-1归一化，有分类器不能为负数，如多项式朴素贝叶斯分类 mm = MinMaxScaler() train_mm_scaled = mm.fit_transform(train_x) test_mm_scaled = mm.transform(test_x) # 4.建立模型，并进行比较 models = {} models['knn'] = KNeighborsClassifier() models['svm'] = SVC() models['bayes'] = MultinomialNB() models['tree'] = DecisionTreeClassifier() models['ada'] = AdaBoostClassifier(base_estimator=models['tree'], learning_rate=0.1) for model_key in models.keys(): if model_key == 'knn' or model_key == 'svm' or model_key == 'ada': model = models[model_key] model.fit(train_ss_scaled, train_y) predict = model.predict(test_ss_scaled) print(model_key, "准确率：", accuracy_score(test_y, predict)) else: model = models[model_key] model.fit(train_mm_scaled, train_y) predict = model.predict(test_mm_scaled) print(model_key, "准确率: ", accuracy_score(test_y, predict)) knn 准确率： 0.9799703264094956 svm 准确率： 0.9985163204747775 bayes 准确率: 0.8835311572700296 tree 准确率: 0.9955489614243324 ada 准确率： 0.9933234421364985 Process finished with exit code 0