局域网wordpress建站,好的活动策划网站,温州 网站建设,做新浪微博网站需要目标#xff1a;运用scikit-learn进行多元线性回归方程的构建#xff0c;通过实际案例的训练集和测试集进行预测#xff0c;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#xff1a;pip install scikit-learn
C:\Users\CMCC\PycharmProjects\AiPro…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-learnpip 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
diabetesdatasets.load_diabetes()
xdiabetes.data
ydiabetes.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_testtrain_test_split(x,y,test_size0.2)#创建一个多元线性回归算法对象
lrLogisticRegression()#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_testlr.predict(x_test)
y_pred_trainlr.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
