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 37.00848745 31.65644737 30.01806775 31.53811995 28.81658937 30.2729268 21.41657477 34.59642857 36.80824105 38.45572278 18.94990323 22.90932064 17.96687546 20.52456809 13.97104962 19.57794875 14.51717616 18.18532608 23.35532398 14.58999303 21.59861695 18.9225969 25.78558708 19.49602977 23.33711976 28.59813875 21.43477898 27.94278691 25.56713646 40.56741206 44.74528008 38.51033543 30.52778586 35.28818885 24.96639727 19.76909304 32.79421099 41.2136618 40.39447199 26.55016423 20.72481448 25.68546388 32.30269711 24.32014753 25.45791115 28.10662487 20.80673346 23.20058813 23.51916194 16.23747476 16.34670006 20.92506088 21.99910974 23.8832463 26.47734736 24.37476018 23.92875684 28.65275141 40.5036973 20.92506088 18.8133716 33.17649957 44.5541358 32.07514438 27.60600887 30.89187022 33.77723876 41.76889045 32.02053173 30.91917654 15.93710516 29.17157162 40.84957744 33.32213331 19.21386439 18.63132942 22.12653927 24.83896774 35.3336994 26.84143172 27.71523418 31.47440519 27.46037513 24.32924964 27.3329456 36.50787146 28.7528746 34.91500238 37.44538868 29.84512768 24.06528848 22.03551818 21.84437389 22.80919744 25.08472469 27.77894894 30.39125422 25.67636177 21.09800095 20.02395209 26.113263 24.93909094 18.03059022 23.08226071 29.41732856 27.86997003 25.31227741 24.44757706 28.88030413 31.19223981 25.54893224 32.86702786 27.66972364 25.72187231 19.68717406 10.59416719 21.05249041 20.15138162 22.3631941 25.1029289 17.25691096 19.15925174 17.95777335 23.41903874 20.97057143 23.81953154 23.36442609 20.31521958 17.28421729 23.71940834 23.86504208 22.78189111 20.69750816 18.74055473 22.9730354 21.2527368 17.26601307 20.22419849 22.81829955 22.76368689 20.27881114 18.74965683 18.98631167 20.47905754 19.80550148 19.65076562 31.23775036 24.85717196 26.27710096 27.89727636 20.06946264 19.01361799 24.63872134 25.72187231 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