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.target
print("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 -0.04687948
0.01549073]
[-0.04547248 -0.04464164 0.03906215 ... 0.02655962 0.04452837
-0.02593034]
[-0.04547248 -0.04464164 -0.0730303 ... -0.03949338 -0.00421986
0.00306441]]
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