人工智能-机器学习之多元线性回归（项目实践一）

目标：运用scikit-learn进行多元线性回归方程的构建，通过实际案例的训练集和测试集进行预测，最终通过预测结果和MSE来评估预测的精度。

一、首先安装scikit-learn：pip install scikit-learn

C:\Users\CMCC\PycharmProjects\AiProject> pip install scikit-learn

二、项目实战：糖尿病预测，你的健康守护者！

from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split#加载糖尿病数据集
diabetes=datasets.load_diabetes()
x=diabetes.data
y=diabetes.targetprint("多元的参数集是:")
print(x)
print("结果集是：")
print(y)#将数据集拆分为训练集和测试集,测试集占20%，训练集占80%
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)#创建一个多元线性回归算法对象
lr=LogisticRegression()#使用训练集训练模型
lr.fit(x_train,y_train)#使用测试集进行结果的预测
y_pred_test=lr.predict(x_test)
y_pred_train=lr.predict(x_train)print("测试集的预测结果是：")
print(y_pred_test)
print("训练集的预测结果是：")
print(y_pred_train)#打印模型的均方差，只保留两位的小数点，分别对于训练集和测试集的均方差进行对比，越小越好，证明越预测得准确
print("均方差：%.2f" % mean_squared_error(y_train ,y_pred_train))
print("均方差：%.2f" % mean_squared_error(y_test ,y_pred_test))多元的参数集是:
[[ 0.03807591  0.05068012  0.06169621 ... -0.00259226  0.01990842-0.01764613][-0.00188202 -0.04464164 -0.05147406 ... -0.03949338 -0.06832974-0.09220405][ 0.08529891  0.05068012  0.04445121 ... -0.00259226  0.00286377-0.02593034]...[ 0.04170844  0.05068012 -0.01590626 ... -0.01107952 -0.046879480.01549073][-0.04547248 -0.04464164  0.03906215 ...  0.02655962  0.04452837-0.02593034][-0.04547248 -0.04464164 -0.0730303  ... -0.03949338 -0.004219860.00306441]]
结果集是：
[151.  75. 141. 206. 135.  97. 138.  63. 110. 310. 101.  69. 179. 185.118. 171. 166. 144.  97. 168.  68.  49.  68. 245. 184. 202. 137.  85.131. 283. 129.  59. 341.  87.  65. 102. 265. 276. 252.  90. 100.  55.61.  92. 259.  53. 190. 142.  75. 142. 155. 225.  59. 104. 182. 128.52.  37. 170. 170.  61. 144.  52. 128.  71. 163. 150.  97. 160. 178.48. 270. 202. 111.  85.  42. 170. 200. 252. 113. 143.  51.  52. 210.65. 141.  55. 134.  42. 111.  98. 164.  48.  96.  90. 162. 150. 279.92.  83. 128. 102. 302. 198.  95.  53. 134. 144. 232.  81. 104.  59.246. 297. 258. 229. 275. 281. 179. 200. 200. 173. 180.  84. 121. 161.99. 109. 115. 268. 274. 158. 107.  83. 103. 272.  85. 280. 336. 281.118. 317. 235.  60. 174. 259. 178. 128.  96. 126. 288.  88. 292.  71.197. 186.  25.  84.  96. 195.  53. 217. 172. 131. 214.  59.  70. 220.268. 152.  47.  74. 295. 101. 151. 127. 237. 225.  81. 151. 107.  64.138. 185. 265. 101. 137. 143. 141.  79. 292. 178.  91. 116.  86. 122.72. 129. 142.  90. 158.  39. 196. 222. 277.  99. 196. 202. 155.  77.191.  70.  73.  49.  65. 263. 248. 296. 214. 185.  78.  93. 252. 150.77. 208.  77. 108. 160.  53. 220. 154. 259.  90. 246. 124.  67.  72.257. 262. 275. 177.  71.  47. 187. 125.  78.  51. 258. 215. 303. 243.91. 150. 310. 153. 346.  63.  89.  50.  39. 103. 308. 116. 145.  74.45. 115. 264.  87. 202. 127. 182. 241.  66.  94. 283.  64. 102. 200.265.  94. 230. 181. 156. 233.  60. 219.  80.  68. 332. 248.  84. 200.55.  85.  89.  31. 129.  83. 275.  65. 198. 236. 253. 124.  44. 172.114. 142. 109. 180. 144. 163. 147.  97. 220. 190. 109. 191. 122. 230.242. 248. 249. 192. 131. 237.  78. 135. 244. 199. 270. 164.  72.  96.306.  91. 214.  95. 216. 263. 178. 113. 200. 139. 139.  88. 148.  88.243.  71.  77. 109. 272.  60.  54. 221.  90. 311. 281. 182. 321.  58.262. 206. 233. 242. 123. 167.  63. 197.  71. 168. 140. 217. 121. 235.245.  40.  52. 104. 132.  88.  69. 219.  72. 201. 110.  51. 277.  63.118.  69. 273. 258.  43. 198. 242. 232. 175.  93. 168. 275. 293. 281.72. 140. 189. 181. 209. 136. 261. 113. 131. 174. 257.  55.  84.  42.146. 212. 233.  91. 111. 152. 120.  67. 310.  94. 183.  66. 173.  72.49.  64.  48. 178. 104. 132. 220.  57.]
测试集的预测结果是：
[72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.]
训练集的预测结果是：
[72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.72. 72. 72. 72. 72. 72. 72. 72. 72. 72. 72.]
均方差：12075.22
均方差：13446.11