测试 R

如何解决测试 R

目前我正在使用 GBM 模型（梯度提升机）来预测一些速率调用 TIIE。

我将数据分成两部分：训练集和测试集。在我使用 gbm() 的 xgboost package 函数训练我的数据后，如下

    gbm.fit<-gbm(formula=TIIE~.,distribution="gaussian",data=BD_train,n.trees=20000,interaction.depth=6,shrinkage=0.001,cv.folds=5,n.cores=NULL) 

我使用 predict() 函数和我的测试数据进行了预测。结果非常合适，但是当我尝试用另一个数据进行预测时，我没有得到相同的结果。有人知道发生了什么或我做错了什么吗？

prediction<-predict.gbm(object=gbm.fit,newdata=BD_test)

数据为：

structure(list(TIIE_2Y = c(4.975,4.97,5,5.01,4.977,4.95,4.975,4.965,5.001,5.06,5.07,5.04,5.025,5.03,4.955,4.855,4.82,4.83,4.64,4.685,4.63,4.665,4.69,4.615,4.62,4.645,4.689,4.67,4.646,4.79,4.807,4.81,4.86,4.87,4.795,4.787,4.8,4.815,4.805,4.77,4.74,4.731,4.785,4.78,4.771,4.765,4.76,4.75,4.753,4.801,4.72,4.735,4.68,4.701,4.65,4.641,4.7,4.705,4.715,4.775,4.819,4.828,4.865,4.875,4.84,4.822,4.835,4.845,4.831,4.843,4.885,4.89,4.9,4.932,4.925,4.93,4.905,4.893,4.859,4.85,4.871,4.91,4.945,4.908,4.92,4.94,4.98,4.985,4.995,5.039,5.08,5.1,5.12,5.075,5.057,5.005,5.02,5.035,5.015,5.05,4.979,4.99,4.9925,4.993,4.937,4.739,4.725,4.702,4.59,4.565,4.555,4.575,4.57,4.58,4.54,4.53,4.574,4.545,4.52,4.535,4.515,4.485,4.495,4.5,4.51,4.49,4.465,4.45,4.42,4.38,4.34,4.29,4.275,4.24,4.26,4.23,4.273,4.195,4.19,4.185,4.1025,4.155,4.175,4.2,4.178,4.14,4.17,4.276,4.355,4.46,4.551,4.73,4.678,4.576,5.016,4.836,4.817,4.71,4.749,4.635,4.518,4.525,4.48,4.66,4.61,4.714,4.633,4.675,4.761,4.781,4.319,4.283,4.238,4.1164,4.073,4.094,4.16,4.135,4.074,4.076,4.075,4.13,4.1,3.935,3.98,3.99,3.952,4.22,4.21,4.208,4.232,4.245,4.187,4.235,4.213,4.154,4.18,4.229,4.215,4.15,4.166,4.268,4.266,4.285,4.295,4.32,4.43,4.375,4.343,4.37,4.36,4.379,4.35,4.282,4.33,4.301,4.262,4.28,4.278,4.3,4.349,4.332,4.31,4.315,4.27,4.254,4.25,4.233,4.292,4.279,4.241,4.255,4.2275,4.1875,4.225,4.248,4.207,3.817,3.84,3.829,3.81,3.82),INF = c(0.041039205,0.04092598,0.040699857,0.040360831,0.040247821,0.040134846,0.039913084,0.039419821,0.03922756,0.039035357,0.03788247,0.037690455,0.037177844,0.037177146,0.037176674,0.037176193,0.037174768,0.03717407,0.037173118,0.037172645,0.037164895,0.037158315,0.037166112,0.037174126,0.037181922,0.037205742,0.037213535,0.037221546,0.037229339,0.037237348,0.03726093,0.037369002,0.037477078,0.037250679,0.035892985,0.035666823,0.035440859,0.03476278,0.034537143,0.034171737,0.033806604,0.033826519,0.033906143,0.033946158,0.033966052,0.034025928,0.03404581,0.034065682,0.034649261,0.03523338,0.035942619,0.036119836,0.036474518,0.037006981,0.037184494,0.037717322,0.03740902,0.037787746,0.037977139,0.038166554,0.038735162,0.039114422,0.039303972,0.039493764,0.040221006,0.040426448,0.040631906,0.040837388,0.041453896,0.041865196,0.04207076,0.042276557,0.042805122,0.042849343,0.042894221,0.043027441,0.043072044,0.043161218,0.043205559,0.043338973,0.04330962,0.04328005,0.043376047,0.043520095,0.043616103,0.043664183,0.043712025,0.043856076,0.04390428,0.044108302,0.044122841,0.044087486,0.044052355,0.043911236,0.04387592,0.043811904,0.043884526,0.043917525,0.04395052,0.043983512,0.0440165,0.044115889,0.044148863,0.044181834,0.044214801,0.044247983,0.044453186,0.044615732,0.044778049,0.044940812,0.045591853,0.045754611,0.04591714,0.046080115,0.04624287,0.04658711,0.046605171,0.046631519,0.04665785,0.046763772,0.046816787,0.046843066,0.046869327,0.046948739,0.04707907,0.047209609,0.047266158,0.047323148,0.047493427,0.047550181,0.04760692,0.047663881,0.047720621,0.047947728,0.047647936,0.047348145,0.047241657,0.046923097,0.046816688,0.046710359,0.046604112,0.046497709,0.046179184,0.045966853,0.045860464,0.046110771,0.046228651,0.046162353,0.046096277,0.04602998,0.045963699,0.045765094,0.04569905,0.045632786,0.045566754,0.043992487,0.043870158,0.043625259,0.043502913,0.043135408,0.043013409,0.042890897,0.042646073,0.04338612,0.042899721,0.041685081,0.041442444,0.041199637,0.040956894,0.039318078,0.039168061,0.039018138,0.038868302,0.038418704,0.038268803,0.038118997,0.037969285,0.037220564,0.0370789,0.036685213,0.035677672,0.035174067,0.034168042,0.033916759,0.033526989,0.033388833,0.033071666,0.032965814,0.032754441,0.032648496,0.032225984,0.03212025,0.032014551,0.031908894,0.03178987,0.031847657,0.031962935,0.032135979,0.03225164,0.03236698,0.032871682,0.03314814,0.033286454,0.033424537,0.03383935,0.034116248,0.03425446,0.034392875,0.03496932,0.035077544,0.035185986,0.035294224,0.035727237,0.035835719,0.035943998,0.036052276,0.036619485,0.03723123,0.037536857,0.038454905,0.039680396,0.04060037,0.041370103,0.04154091,0.042053222,0.042224389,0.042395112,0.043249988,0.043421049,0.043592086,0.044099925,0.044325575,0.045003269,0.045228983,0.045455043,0.045681008,0.046812044,0.047264511,0.047100951,0.046820928,0.046727625,0.04663434,0.046540852,0.046167937,0.046074757,0.045981374,0.04588844,0.046286184,0.046632448,0.046892386,0.046979059,0.047238716,0.046940524,0.046824174,0.046708063,0.046591981,0.046243697,0.046127729,0.046011579,0.045895669,0.045431661,0.045276623,0.045075832,0.044674665,0.044073067,0.043872654,0.043672314,0.043472039,0.043271608,0.042670703,0.042054135,0.041845608,0.041221108,0.040804687,0.040596582,0.040388757,0.039765073,0.0395571,0.039349198,0.039064333,0.038055023,0.037802893,0.037550885,0.037298783,0.036291379,0.036039671,0.035788085,0.035536405,0.035284847,0.035072884,0.034922698,0.034847611,0.03477254,0.034546949,0.034472123,0.034396881,0.034321864,0.03424664,0.034150778,0.034230845,0.034311107,0.034391565,0.0344718,0.034792805,0.034873181,0.03527484,0.035279278,0.03516246,0.034928293,0.034460614,0.034343839,0.034226885,0.034109976,0.03364247,0.033517775,0.033820972,0.033881504,0.03400293,0.034063401,0.034245311,0.034306118,0.034366689,0.034131359,0.034039433,0.033486555,0.03339433,0.03330233,0.03321014,0.032802918,0.032771974,0.032710129,0.032585782,0.032554731,0.032523905,0.032859433,0.032976441,0.033093034,0.033209632,0.034027214,0.034144274,0.033930496,0.033972786,0.034015232,0.034057619,0.034099954,0.034227058,0.03426938,0.034311863,0.03439666,0.034523655,0.035235152,0.035666898,0.03609875,0.036386686,0.03653092,0.036674977,0.037107146,0.037251344,0.037090778,0.037257475,0.037507508,0.037674323,0.037757893,0.037841016,0.038091452,0.038341829,0.038535732,0.038991997,0.039144044,0.039296283,0.039448495,0.039600483,0.040209464,0.040514254,0.040666513,0.041462915,0.042818086,0.043835301,0.044174651,0.044514353,0.044853778,0.045193545,0.04583851,0.045641202,0.045444203,0.044458868,0.044261867,0.044064962,0.043868161,0.043277747,0.043167303,0.043071738,0.042976412,0.042880917,0.042594633,0.042499269,0.042308443,0.0420414,0.042128136,0.04215711,0.042301696,0.04235958,0.042213482,0.04176129,0.041535515,0.040632408,0.040406734,0.040181207,0.039955616,0.039279496,0.038828785,0.038543671,0.038000767,0.037819603,0.037638724,0.03745792,0.036734412,0.036553775,0.036373206,0.036192511,0.036291896,0.036100361,0.036036636,0.035972903,0.035909163,0.035590326,0.035526739,0.035399131,0.035185871,0.035146051,0.035066413,0.035026387,0.034866909,0.034826891,0.034786867,0.034747051,0.034881523,0.034866512,0.034851703,0.034836679,0.03482185,0.03477711,0.034762047,0.034747179,0.034732297,0.034717416,0.034672707,0.034510906,0.03469019,0.034959309),SPRD_MX_USA_5A = c(4.536,4.541,4.634,4.625,4.6105,4.492,4.4375,4.4165,4.4285,4.466,4.4146,4.4735,4.482,4.428,4.366,4.259,4.348,4.4615,4.3475,4.247,4.2695,4.3095,4.3315,4.269,4.2495,4.226,4.284,4.289,4.3085,4.3445,4.425,4.4415,4.437,4.473,4.305,4.3185,4.344,4.4085,4.3485,4.4035,4.3195,4.418,4.454,4.3785,4.347,4.3405,4.4275,4.369,4.4155,4.3725,4.2205,4.2625,4.2915,4.3685,4.261,4.204,4.186,4.2365,4.1825,4.169,4.1242,4.257,4.1835,4.1643,4.2191,4.242,4.318,4.2723,4.296,4.304,4.3186,4.267,4.2899,4.277,4.3134,4.351,4.401,4.41,4.4095,4.4225,4.4589,4.451,4.4634,4.4995,4.4758,4.452,4.421,4.372,4.3884,4.419,4.4915,4.47,4.416,4.374,4.3977,4.391,4.4255,4.4055,4.4175,4.393,4.4625,4.487,4.441,4.599,4.614,4.619,4.629,4.603,4.5725,4.5226,4.4909,4.4797,4.537,4.552,4.5395,4.5175,4.4964,4.5101,4.538,4.588,4.512,4.469,4.5205,4.493,4.5075,4.439,4.447,4.4435,4.477,4.4665,4.4605,4.444,4.376,4.358,4.2739,4.131,4.1536,4.11,4.038,4.03,3.955,3.961,3.89,3.824,3.932,3.9397,3.853,3.8461,3.905,3.907,3.9384,3.9352,3.977,4.008,4.0185,4.011,3.9051,3.859,3.869,3.8667,3.854,3.8916,3.9315,3.95,3.92,3.9635,3.9745,3.973,3.9752,3.909,3.8015,3.808,3.842,3.861,3.8,3.7771,3.69,3.724,3.725,3.717,3.738,3.7705,3.706,3.7255,3.6575,3.547,3.586,3.622,3.64,3.6625,3.6345,3.6695,3.688,3.718,3.768,4.0337,3.982,3.819,4.0802,4.202,4.0645,4.08,4.018,4.091,4.197,4.5378,4.3215,4.3455,4.134,4.0735,4.0784,4.077,4.097,4.122,4.105,3.9904,3.942,3.966,3.892,3.956,4.009,3.941,3.827,3.873,4.005,4.124,4.1721,4.148,4.107,4.0105,4.0345,4.022,3.903,3.921,4.066,4.098,4.141,4.246,4.1345,4.099,4.0315,4.132,4.2414,4.2005,4.2605,4.168,3.836,3.741,3.723,3.619,3.6925,3.72,3.504,3.483,3.666,3.757,3.7975,3.746,3.726,3.685,3.6655,3.749,3.7145,3.443,3.506,3.575,3.552,3.5365,3.5635,3.823,3.797,3.811,3.915,3.943,3.9475,3.953,3.8955,3.984,3.934,3.9495,3.9002,3.862,3.815,3.849,3.898,3.881,3.784,3.778,3.732,3.703,3.673,3.6765,3.693,3.75,3.764,3.779,3.759,3.7815,3.844,3.9085,3.899,3.887,3.889,3.902,4.013,3.9795,4.153,4.189,4.065,4.017,3.9112,3.8785,3.894,3.8705,3.834,3.835,3.8825,3.9185,3.8817,3.9055,3.821,3.788,3.838,3.82,3.692,3.668,3.702,3.684,3.697,3.6725,3.681,3.7347,3.7575,3.701,3.658,3.6805,3.696,3.704,3.698,3.6155,3.623,3.6232,3.609,3.608,3.649,3.616,3.68,3.652,3.625,3.6195,3.646,3.6543,3.6395,3.639,3.645,3.647,3.2245,3.2092,3.2295,3.239,3.282),SPRD_MX_USA_10A = 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= "data.frame")

使用测试集的预测值与实际值的关系图如下

with test set

但是当我在另一个日期（不同的值）中使用另一个具有相同功能的集合时，我得到了这个

with another data but same features

我很感激有人可以帮助我。