广义的预测任务中，要求估计连续型预测值时，是“回归任务”；要求判断因变量属于哪个类别时，是”分类“任务
#-*- coding: utf-8 -*- ''' Created on 2018年3月6日 boston.DESCR:解释:: 波士顿房价数据集 - - - - - - 数据集特征: 实例数量:506 属性数量:13个数字/分类预测。 中值(属性14)通常是目标。 属性信息(按顺序): -按城镇的人均犯罪率计算。 -住宅用地的ZN比例超过25,000平方英尺。 -每个城镇非零售商业用地的比例。 - CHAS Charles河哑变量(= 1，如果有边界河;0否则) -氮氧化物浓度(每1000万部分) -每个住宅的平均房间数。 -在1940年以前建造的自有单位的年龄比例。 - DIS加权距离到5个波士顿就业中心。 -径向高速公路的可达性拉德指数。 -税收全值财产税税率为1万美元。 -学生与教师的比例。 1000 - B(Bk - 0.63)^ 2,Bk由城镇黑人的比例 - LSTAT %低的人口状况。 -业主自住房屋的中值为$1000。 缺少属性值:没有 作者:哈里森，D.和Rubinfeld, D.L.。 这是UCI ML住房数据集的副本。 http://archive.ics.uci.edu/ml/datasets/Housing 这个数据集取自于卡内基梅隆大学的StatLib图书馆。 哈里森，D.和Rubinfeld, D.L.的波士顿房价数据。“享乐 环境空气的价格和需求。经济学与管理, 第五卷,81 - 102,81。用于贝尔斯利，Kuh & Welsch，“回归诊断”。 …“威利,1980。表中使用了各种转换。 第244-261页。 波士顿的房价数据已经在许多机器学习论文中使用，以解决回归问题。 * * * *的引用 - Belsley, Kuh & Welsch，“回归诊断:识别有影响力的数据和共线性的来源”，Wiley, 1980。244 - 261。 -更多!(参见http://archive.ics.uci.edu/ml/datasets/Housing) @author: soyo ''' from sklearn import datasets from sklearn.linear_model import LinearRegression import numpy as np import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties font = FontProperties(fname = "/usr/share/fonts/truetype/arphic/ukai.ttc", size=14) #解决python绘图中文乱码 # np.set_printoptions(threshold=np.inf) #目的是将print省略的部分都输出 boston=datasets.load_boston() print boston.data print boston.keys() print boston.feature_names print boston.target #表示对应的房价 x=boston.data[:,np.newaxis,5] #以每个元素对应一个列表返回数据集指定的第６列 print len(x) print "住宅平均房间数：",x y=boston.target lm=LinearRegression() # x1=[[1],[2],[3],[4],[5]] # y1=[6,7,8,9,2] lm.fit(x,y) #训练模型 # lm.fit(x1,y1) #训练模型 print u"方程的确定性系数:%.2f"% lm.score(x, y) # print u"方程的确定性系数:%.2f"% lm.score(x1, y1) print "回归方程的斜率为:",lm.coef_ print "回归方程的截距为:",lm.intercept_ print '回归方程为：y = ', lm.coef_, '*x + (', lm.intercept_, ')' # print lm.predict(x1) print lm.predict(x) plt.scatter(x,y,color="blue") # plt.scatter(x1,y1,color="blue") plt.plot(x,lm.predict(x),color="red",linewidth=3) # plt.plot(x1,lm.predict(x1),color="red",linewidth=3) plt.xlabel(u"平均房间数目",fontproperties=font) plt.ylabel(u"房价",fontproperties=font) plt.title(u"",fontproperties=font) plt.show()
结果：
这个是注释掉的代码产生的结果：
[[ 6.32000000e-03 1.80000000e+01 2.31000000e+00 ..., 1.53000000e+01
3.96900000e+02 4.98000000e+00]
[ 2.73100000e-02 0.00000000e+00 7.07000000e+00 ..., 1.78000000e+01
3.96900000e+02 9.14000000e+00]
[ 2.72900000e-02 0.00000000e+00 7.07000000e+00 ..., 1.78000000e+01
3.92830000e+02 4.03000000e+00]
...,
[ 6.07600000e-02 0.00000000e+00 1.19300000e+01 ..., 2.10000000e+01
3.96900000e+02 5.64000000e+00]
[ 1.09590000e-01 0.00000000e+00 1.19300000e+01 ..., 2.10000000e+01
3.93450000e+02 6.48000000e+00]
[ 4.74100000e-02 0.00000000e+00 1.19300000e+01 ..., 2.10000000e+01
3.96900000e+02 7.88000000e+00]]
['data', 'feature_names', 'DESCR', 'target']
['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'
'B' 'LSTAT']
[ 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]
506
住宅平均房间数： [[ 6.575]
[ 6.421]
[ 7.185]
[ 6.998]
[ 7.147]
[ 6.43 ]
[ 6.012]
[ 6.172]
[ 5.631]
[ 6.004]
[ 6.377]
[ 6.009]
[ 5.889]
[ 5.949]
[ 6.096]
[ 5.834]
[ 5.935]
[ 5.99 ]
[ 5.456]
[ 5.727]
[ 5.57 ]
[ 5.965]
[ 6.142]
[ 5.813]
[ 5.924]
[ 5.599]
[ 5.813]
[ 6.047]
[ 6.495]
[ 6.674]
[ 5.713]
[ 6.072]
[ 5.95 ]
[ 5.701]
[ 6.096]
[ 5.933]
[ 5.841]
[ 5.85 ]
[ 5.966]
[ 6.595]
[ 7.024]
[ 6.77 ]
[ 6.169]
[ 6.211]
[ 6.069]
[ 5.682]
[ 5.786]
[ 6.03 ]
[ 5.399]
[ 5.602]
[ 5.963]
[ 6.115]
[ 6.511]
[ 5.998]
[ 5.888]
[ 7.249]
[ 6.383]
[ 6.816]
[ 6.145]
[ 5.927]
[ 5.741]
[ 5.966]
[ 6.456]
[ 6.762]
[ 7.104]
[ 6.29 ]
[ 5.787]
[ 5.878]
[ 5.594]
[ 5.885]
[ 6.417]
[ 5.961]
[ 6.065]
[ 6.245]
[ 6.273]
[ 6.286]
[ 6.279]
[ 6.14 ]
[ 6.232]
[ 5.874]
[ 6.727]
[ 6.619]
[ 6.302]
[ 6.167]
[ 6.389]
[ 6.63 ]
[ 6.015]
[ 6.121]
[ 7.007]
[ 7.079]
[ 6.417]
[ 6.405]
[ 6.442]
[ 6.211]
[ 6.249]
[ 6.625]
[ 6.163]
[ 8.069]
[ 7.82 ]
[ 7.416]
[ 6.727]
[ 6.781]
[ 6.405]
[ 6.137]
[ 6.167]
[ 5.851]
[ 5.836]
[ 6.127]
[ 6.474]
[ 6.229]
[ 6.195]
[ 6.715]
[ 5.913]
[ 6.092]
[ 6.254]
[ 5.928]
[ 6.176]
[ 6.021]
[ 5.872]
[ 5.731]
[ 5.87 ]
[ 6.004]
[ 5.961]
[ 5.856]
[ 5.879]
[ 5.986]
[ 5.613]
[ 5.693]
[ 6.431]
[ 5.637]
[ 6.458]
[ 6.326]
[ 6.372]
[ 5.822]
[ 5.757]
[ 6.335]
[ 5.942]
[ 6.454]
[ 5.857]
[ 6.151]
[ 6.174]
[ 5.019]
[ 5.403]
[ 5.468]
[ 4.903]
[ 6.13 ]
[ 5.628]
[ 4.926]
[ 5.186]
[ 5.597]
[ 6.122]
[ 5.404]
[ 5.012]
[ 5.709]
[ 6.129]
[ 6.152]
[ 5.272]
[ 6.943]
[ 6.066]
[ 6.51 ]
[ 6.25 ]
[ 7.489]
[ 7.802]
[ 8.375]
[ 5.854]
[ 6.101]
[ 7.929]
[ 5.877]
[ 6.319]
[ 6.402]
[ 5.875]
[ 5.88 ]
[ 5.572]
[ 6.416]
[ 5.859]
[ 6.546]
[ 6.02 ]
[ 6.315]
[ 6.86 ]
[ 6.98 ]
[ 7.765]
[ 6.144]
[ 7.155]
[ 6.563]
[ 5.604]
[ 6.153]
[ 7.831]
[ 6.782]
[ 6.556]
[ 7.185]
[ 6.951]
[ 6.739]
[ 7.178]
[ 6.8 ]
[ 6.604]
[ 7.875]
[ 7.287]
[ 7.107]
[ 7.274]
[ 6.975]
[ 7.135]
[ 6.162]
[ 7.61 ]
[ 7.853]
[ 8.034]
[ 5.891]
[ 6.326]
[ 5.783]
[ 6.064]
[ 5.344]
[ 5.96 ]
[ 5.404]
[ 5.807]
[ 6.375]
[ 5.412]
[ 6.182]
[ 5.888]
[ 6.642]
[ 5.951]
[ 6.373]
[ 6.951]
[ 6.164]
[ 6.879]
[ 6.618]
[ 8.266]
[ 8.725]
[ 8.04 ]
[ 7.163]
[ 7.686]
[ 6.552]
[ 5.981]
[ 7.412]
[ 8.337]
[ 8.247]
[ 6.726]
[ 6.086]
[ 6.631]
[ 7.358]
[ 6.481]
[ 6.606]
[ 6.897]
[ 6.095]
[ 6.358]
[ 6.393]
[ 5.593]
[ 5.605]
[ 6.108]
[ 6.226]
[ 6.433]
[ 6.718]
[ 6.487]
[ 6.438]
[ 6.957]
[ 8.259]
[ 6.108]
[ 5.876]
[ 7.454]
[ 8.704]
[ 7.333]
[ 6.842]
[ 7.203]
[ 7.52 ]
[ 8.398]
[ 7.327]
[ 7.206]
[ 5.56 ]
[ 7.014]
[ 8.297]
[ 7.47 ]
[ 5.92 ]
[ 5.856]
[ 6.24 ]
[ 6.538]
[ 7.691]
[ 6.758]
[ 6.854]
[ 7.267]
[ 6.826]
[ 6.482]
[ 6.812]
[ 7.82 ]
[ 6.968]
[ 7.645]
[ 7.923]
[ 7.088]
[ 6.453]
[ 6.23 ]
[ 6.209]
[ 6.315]
[ 6.565]
[ 6.861]
[ 7.148]
[ 6.63 ]
[ 6.127]
[ 6.009]
[ 6.678]
[ 6.549]
[ 5.79 ]
[ 6.345]
[ 7.041]
[ 6.871]
[ 6.59 ]
[ 6.495]
[ 6.982]
[ 7.236]
[ 6.616]
[ 7.42 ]
[ 6.849]
[ 6.635]
[ 5.972]
[ 4.973]
[ 6.122]
[ 6.023]
[ 6.266]
[ 6.567]
[ 5.705]
[ 5.914]
[ 5.782]
[ 6.382]
[ 6.113]
[ 6.426]
[ 6.376]
[ 6.041]
[ 5.708]
[ 6.415]
[ 6.431]
[ 6.312]
[ 6.083]
[ 5.868]
[ 6.333]
[ 6.144]
[ 5.706]
[ 6.031]
[ 6.316]
[ 6.31 ]
[ 6.037]
[ 5.869]
[ 5.895]
[ 6.059]
[ 5.985]
[ 5.968]
[ 7.241]
[ 6.54 ]
[ 6.696]
[ 6.874]
[ 6.014]
[ 5.898]
[ 6.516]
[ 6.635]
[ 6.939]
[ 6.49 ]
[ 6.579]
[ 5.884]
[ 6.728]
[ 5.663]
[ 5.936]
[ 6.212]
[ 6.395]
[ 6.127]
[ 6.112]
[ 6.398]
[ 6.251]
[ 5.362]
[ 5.803]
[ 8.78 ]
[ 3.561]
[ 4.963]
[ 3.863]
[ 4.97 ]
[ 6.683]
[ 7.016]
[ 6.216]
[ 5.875]
[ 4.906]
[ 4.138]
[ 7.313]
[ 6.649]
[ 6.794]
[ 6.38 ]
[ 6.223]
[ 6.968]
[ 6.545]
[ 5.536]
[ 5.52 ]
[ 4.368]
[ 5.277]
[ 4.652]
[ 5. ]
[ 4.88 ]
[ 5.39 ]
[ 5.713]
[ 6.051]
[ 5.036]
[ 6.193]
[ 5.887]
[ 6.471]
[ 6.405]
[ 5.747]
[ 5.453]
[ 5.852]
[ 5.987]
[ 6.343]
[ 6.404]
[ 5.349]
[ 5.531]
[ 5.683]
[ 4.138]
[ 5.608]
[ 5.617]
[ 6.852]
[ 5.757]
[ 6.657]
[ 4.628]
[ 5.155]
[ 4.519]
[ 6.434]
[ 6.782]
[ 5.304]
[ 5.957]
[ 6.824]
[ 6.411]
[ 6.006]
[ 5.648]
[ 6.103]
[ 5.565]
[ 5.896]
[ 5.837]
[ 6.202]
[ 6.193]
[ 6.38 ]
[ 6.348]
[ 6.833]
[ 6.425]
[ 6.436]
[ 6.208]
[ 6.629]
[ 6.461]
[ 6.152]
[ 5.935]
[ 5.627]
[ 5.818]
[ 6.406]
[ 6.219]
[ 6.485]
[ 5.854]
[ 6.459]
[ 6.341]
[ 6.251]
[ 6.185]
[ 6.417]
[ 6.749]
[ 6.655]
[ 6.297]
[ 7.393]
[ 6.728]
[ 6.525]
[ 5.976]
[ 5.936]
[ 6.301]
[ 6.081]
[ 6.701]
[ 6.376]
[ 6.317]
[ 6.513]
[ 6.209]
[ 5.759]
[ 5.952]
[ 6.003]
[ 5.926]
[ 5.713]
[ 6.167]
[ 6.229]
[ 6.437]
[ 6.98 ]
[ 5.427]
[ 6.162]
[ 6.484]
[ 5.304]
[ 6.185]
[ 6.229]
[ 6.242]
[ 6.75 ]
[ 7.061]
[ 5.762]
[ 5.871]
[ 6.312]
[ 6.114]
[ 5.905]
[ 5.454]
[ 5.414]
[ 5.093]
[ 5.983]
[ 5.983]
[ 5.707]
[ 5.926]
[ 5.67 ]
[ 5.39 ]
[ 5.794]
[ 6.019]
[ 5.569]
[ 6.027]
[ 6.593]
[ 6.12 ]
[ 6.976]
[ 6.794]
[ 6.03 ]]
方程的确定性系数:0.48
回归方程的斜率为: [ 9.10210898]
回归方程的截距为: -34.6706207764
回归方程为：y = [ 9.10210898] *x + ( -34.6706207764 )
[ 25.17574577 23.77402099 30.72803225 29.02593787 30.38215211
23.85593997 20.05125842 21.50759586 16.5833549 19.97844155
23.3735282 20.02395209 18.93169901 19.47782555 20.81583557
18.43108302 19.35039603 19.85101202 14.99048582 17.45715736
16.02812625 19.6234593 21.23453259 18.23993873 19.25027283
16.29208741 18.23993873 20.36983223 24.44757706 26.07685456
17.32972783 20.59738496 19.48692766 17.22050253 20.81583557
19.33219181 18.49479778 18.57671676 19.63256141 25.35778795
29.26259271 26.95065703 21.48028953 21.86257811 20.57007863
17.04756245 17.99418179 20.21509638 14.47166561 16.31939374
19.60525508 20.98877564 24.5932108 19.92382889 18.9225969
31.31056723 23.42814085 27.36935404 21.26183891 19.27757916
17.58458688 19.63256141 24.09259481 26.87784015 29.99076143
22.58164472 18.0032839 18.83157581 16.24657686 18.89529058
23.73761256 19.58705086 20.53367019 22.17204981 22.42690886
22.54523628 22.48152152 21.21632837 22.05372239 18.79516738
26.55926634 25.57623857 22.69087002 21.46208531 23.4827535
25.67636177 20.07856475 21.0433883 29.10785685 29.7632087
23.73761256 23.62838725 23.96516528 21.86257811 22.20845825
25.63085122 21.42567687 38.77429659 36.50787146 32.83061943
26.55926634 27.05078022 23.62838725 21.18902204 21.46208531
18.58581887 18.44928724 21.09800095 24.25643277 22.02641607
21.71694436 26.45004103 19.15014963 20.77942714 22.25396879
19.28668126 21.54400429 20.1331774 18.77696316 17.49356579
18.75875894 19.97844155 19.58705086 18.63132942 18.84067792
19.81460358 16.41951693 17.14768565 23.86504208 16.63796755
24.11079902 22.90932064 23.32801765 18.32185771 17.73022063
22.99123962 19.41411079 24.07439059 18.64043153 21.31645157
21.52580007 11.0128642 14.50807405 15.09971113 9.95701956
21.12530728 16.55604857 10.16636806 12.5329164 16.27388319
21.05249041 14.51717616 10.94914944 17.2933194 21.11620517
21.32555368 13.31569777 28.52532188 20.5427723 24.58410869
22.21756036 33.49507338 36.34403349 41.55954194 18.6131252
20.86134612 37.50000134 18.82247371 22.84560588 23.60108092
18.80426949 18.84978003 16.04633047 23.72851045 18.65863574
24.91178461 20.12407529 22.80919744 27.76984683 28.86209991
36.00725546 21.2527368 30.45496898 25.06652047 16.33759795
21.33465578 36.60799466 27.05988233 25.0028057 30.72803225
28.59813875 26.66849165 30.66431749 27.2237203 25.43970694
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