from sklearn import datasets
from sklearn.linear_model import LinearRegression
loaded_data = datasets.load_boston()
data_X = loaded_data.data
data_y = loaded_data.target
print(data_y)
[ 24. 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15. 18.9
21.7 20.4 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5
15.6 13.9 16.6 14.8 18.4 21. 12.7 14.5 13.2 13.1 13.5 18.9
20. 21. 24.7 30.8 34.9 26.6 25.3 24.7 21.2 19.3 20. 16.6
14.4 19.4 19.7 20.5 25. 23.4 18.9 35.4 24.7 31.6 23.3 19.6
18.7 16. 22.2 25. 33. 23.5 19.4 22. 17.4 20.9 24.2 21.7
22.8 23.4 24.1 21.4 20. 20.8 21.2 20.3 28. 23.9 24.8 22.9
23.9 26.6 22.5 22.2 23.6 28.7 22.6 22. 22.9 25. 20.6 28.4
21.4 38.7 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4
19.8 19.4 21.7 22.8 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3
22. 20.3 20.5 17.3 18.8 21.4 15.7 16.2 18. 14.3 19.2 19.6
23. 18.4 15.6 18.1 17.4 17.1 13.3 17.8 14. 14.4 13.4 15.6
11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4 17. 15.6
13.1 41.3 24.3 23.3 27. 50. 50. 50. 22.7 25. 50. 23.8
23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2
39.8 36.2 37.9 32.5 26.4 29.6 50. 32. 29.8 34.9 37. 30.5
36.4 31.1 29.1 50. 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5
50. 22.6 24.4 22.5 24.4 20. 21.7 19.3 22.4 28.1 23.7 25.
23.3 28.7 21.5 23. 26.7 21.7 27.5 30.1 44.8 50. 37.6 31.6
46.7 31.5 24.3 31.7 41.7 48.3 29. 24. 25.1 31.5 23.7 23.3
22. 20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8
29.6 42.8 21.9 20.9 44. 50. 36. 30.1 33.8 43.1 48.8 31.
36.5 22.8 30.7 50. 43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.
33.2 33.1 29.1 35.1 45.4 35.4 46. 50. 32.2 22. 20.1 23.2
22.3 24.8 28.5 37.3 27.9 23.9 21.7 28.6 27.1 20.3 22.5 29.
24.8 22. 26.4 33.1 36.1 28.4 33.4 28.2 22.8 20.3 16.1 22.1
19.4 21.6 23.8 16.2 17.8 19.8 23.1 21. 23.8 23.1 20.4 18.5
25. 24.6 23. 22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1
19.5 18.5 20.6 19. 18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1
24.5 26.6 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6
25. 19.9 20.8 16.8 21.9 27.5 21.9 23.1 50. 50. 50. 50.
50. 13.8 13.8 15. 13.9 13.3 13.1 10.2 10.4 10.9 11.3 12.3
8.8 7.2 10.5 7.4 10.2 11.5 15.1 23.2 9.7 13.8 12.7 13.1
12.5 8.5 5. 6.3 5.6 7.2 12.1 8.3 8.5 5. 11.9 27.9
17.2 27.5 15. 17.2 17.9 16.3 7. 7.2 7.5 10.4 8.8 8.4
16.7 14.2 20.8 13.4 11.7 8.3 10.2 10.9 11. 9.5 14.5 14.1
16.1 14.3 11.7 13.4 9.6 8.7 8.4 12.8 10.5 17.1 18.4 15.4
10.8 11.8 14.9 12.6 14.1 13. 13.4 15.2 16.1 17.8 14.9 14.1
12.7 13.5 14.9 20. 16.4 17.7 19.5 20.2 21.4 19.9 19. 19.1
19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3 16.7 12. 14.6 21.4
23. 23.7 25. 21.8 20.6 21.2 19.1 20.6 15.2 7. 8.1 13.6
20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9
22. 11.9]
#线性回归
model = LinearRegression()
model.fit(data_X,data_y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
#对比前四个值与预测值
print(model.predict(data_X[:4,:]))
print(data_y[:4])
[ 30.00821269 25.0298606 30.5702317 28.60814055]
[ 24. 21.6 34.7 33.4]
#创造数据
X,y = datasets.make_regression(n_samples=100,n_features=1,n_targets=1,noise=1)
print(X,y)
[[ 0.53484728]
[ 0.09466198]
[ 1.34507271]
[-1.31088444]
[ 0.58025056]
[-1.10096713]
[ 0.7857833 ]
[-0.51311647]
[-0.6465785 ]
[ 0.92960333]
[ 1.38840659]
[ 0.19348591]
[-1.1664881 ]
[ 0.75207622]
[ 0.64580054]
[-1.91580043]
[ 1.3506 ]
[ 1.78021531]
[-0.10269969]
[-1.45425265]
[-1.32332361]
[ 0.0621699 ]
[-0.39986477]
[-1.12802116]
[ 1.7483509 ]
[ 1.32292197]
[-0.37384565]
[ 0.28216756]
[-1.47542569]
[ 1.5479627 ]
[ 0.06995328]
[ 0.2952387 ]
[ 0.42768088]
[ 0.450825 ]
[ 0.88578399]
[ 0.85147996]
[-1.25617283]
[-0.03551764]
[-0.50614323]
[-0.07338338]
[-1.02351513]
[-1.18741664]
[-0.01208598]
[-1.6367255 ]
[ 0.8804416 ]
[ 0.35813584]
[ 0.12182145]
[ 0.05819713]
[-0.10406682]
[ 0.88863063]
[-0.95036244]
[ 0.89741136]
[-2.0761787 ]
[-1.92279711]
[ 1.01815836]
[ 0.65187508]
[ 0.57378986]
[-0.09858105]
[-0.23085505]
[ 0.58347627]
[ 1.33470489]
[ 1.24853412]
[-0.62648686]
[ 1.1155054 ]
[ 0.33504972]
[ 0.48904758]
[-1.74268143]
[-2.0367449 ]
[ 0.53431475]
[ 0.0611812 ]
[ 1.70804096]
[ 1.08174664]
[-0.74144157]
[ 1.41634367]
[-1.50642148]
[ 2.03578117]
[ 1.36719829]
[ 1.00704541]
[-0.88289054]
[-0.67625075]
[ 1.60697398]
[-0.37793002]
[-1.57440118]
[ 1.09009579]
[-0.44940828]
[ 1.64963222]
[-1.8369536 ]
[-0.65285564]
[ 0.0198095 ]
[ 0.03472977]
[ 0.4685963 ]
[-0.85911355]
[-0.30220529]
[ 0.55899829]
[ 1.21070012]
[-1.07870773]
[-0.11221986]
[-0.35157916]
[-0.06284252]
[-1.10413217]] [ 35.04137923 6.67445206 89.1887115 -85.86451558 36.98686662
-72.92821527 51.86682499 -34.69786395 -42.83551589 61.82103167
92.73673928 11.58049078 -76.09868009 49.93617116 43.02138599
-126.09276203 89.87412595 118.54962007 -5.62382095 -96.79508494
-86.66287053 5.29155172 -27.69674613 -76.07923267 116.87861441
88.35398414 -23.89284497 19.63198733 -97.40042846 104.40656187
4.97508379 18.2269232 27.87053187 29.50816411 61.10341249
59.78170322 -85.71373262 -3.91712594 -34.35006381 -5.77679031
-67.51920355 -79.55160435 -1.99765897 -108.08238882 59.79700843
24.60768426 8.23752438 3.4575212 -7.53113032 59.87988435
-62.87627739 60.71576618 -138.47992707 -128.29409295 67.91874014
44.8192337 37.42835412 -5.34690779 -16.25498474 39.48704617
88.2313693 82.23809065 -42.16653969 74.44990164 21.38691013
32.2923918 -114.88114137 -136.1756398 36.16201735 3.35286591
110.82367051 71.60760825 -48.4528432 94.7096711 -99.83200908
135.61594923 91.15560295 66.09513697 -59.51164066 -46.04753082
103.77034245 -24.94603219 -104.40981804 73.57428866 -30.2955937
109.16199455 -122.47711418 -43.37879225 3.82008495 3.20863493
32.99653935 -56.2763452 -20.74694624 36.67869052 79.67552968
-71.31635282 -6.55670787 -23.22650451 -3.62246158 -73.72908692]
#绘制散点图
import matplotlib.pyplot as plot
plot.scatter(X,y)
plot.show()