Commit ef42830b authored by John Doe's avatar John Doe
Browse files

Minor tweaks to Main work-flow defaults

parent 7645024b
......@@ -501,8 +501,8 @@ write_evals <- function( hist_bins = 25, write_errors=T, Surrogate_performance_t
# - together with specified preprocessing methods (see header for method enum descriptions)
# - and column rounding specification. the rounding spec is important because otherwise You
# may end up trying to predict numerical effects in the model output instead of meaningful output
Main <- function(surrogate_types = c(),
preprocessing_ind = c(17,22,23,24,25,26)
Main <- function(surrogate_types = c()
,preprocessing_ind = c(17)
# set this to actual data str to supply loaded tables
,input_data = "dumps/amd_course_IN.txt"
,output_data = "dumps/amd_course_OUT.txt"
......@@ -532,20 +532,20 @@ Main <- function(surrogate_types = c(),
# of ensemble and will not predicted in output, but will be given as inputs in validation.
# the reasoning is that these parameters come from the couple model (flow?) and are thus
# external to chemistry - are therefore forced on the surrogate completely from the outside
,external=c("Time","Pressure")
,allow_neg_cols=c("Pressure","Charge")
,external=c()
,allow_neg_cols=T
#,multiround = c(9,6)
,multiround = c()
#,exclude_output_columns = c("e")
,train_para=T, run_para=F, preproc_para=T, use_cores = 0
,train_para=F, run_para=F, preproc_para=T, use_cores = 0 # 0 = auto
,tuner = F
# you can apply a rounding specification, but it usually does more harm than good
# you can apply a rounding specification, including for each output column, like tis
#,r_digits = list( C=f6,Ca=6,Cl=6,H=12,Mg=5,e=9,Calcite=6,Dolomite=6
# or just set it to Inf for no rounding
,r_digits = Inf
,write_model_data=T
,write_full_residuals=F
,write_filepath_prefix="models/"
#,r_digits =
#,r_digits = list( C=f6,Ca=6,Cl=6,H=12,Mg=5,e=9,Calcite=6,Dolomite=6 )
) {
# run only the preprocessing methods that have not been blacklisted
......
output,method,preprocessing,speed,method_realname,RMSE,RSS,SAD,MAD,MASE,AME,RSSQ,model_id,MASE_speed_score
medv,mda_p01,no,0.00100000000020373,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,1,0.0182663973456695
medv,polymars_p01,no,0.00900000000001455,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,2,0.164397576077799
medv,rpart_anova_direct,no,0.00300000000061118,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,3,0.0547991920370086
medv,bayesglm,no,0.00200000000040745,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,4,0.0365327946913391
medv,mda_p01,no,0.00300000000000011,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,1,0.05119769329847
medv,polymars_p01,no,0.111999999999995,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,2,1.91138054980938
medv,rpart_anova_direct,no,0.00400000000000489,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,3,0.0682635910647075
medv,bayesglm,no,0.046999999999997,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,4,0.802097195009282
medv,rlm,no,0,failed @ fitting,,,,,,,,5,
medv,gaussprLinear,no,0.110000000000582,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,6,2.00930370762493
medv,glm,no,0.00200000000040745,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,7,0.0365327946913391
medv,lmStepAIC,no,0.00199999999949796,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,8,0.0365327946747259
medv,glmStepAIC,no,0.00200000000040745,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,9,0.0365327946913391
medv,leapBackward,no,0.0509999999994761,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,10,0.931586264429788
medv,leapForward,no,0.0510000000003856,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,11,0.931586264446401
medv,leapSeq,no,0.0509999999994761,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,12,0.931586264429788
medv,treebag,no,0.0140000000001237,Bagged CART,5.32464612728951,4309.48216989059,484.310059153108,3.18625038916519,0.582725226886745,26.7140574480307,65.6466462958359,13,0.255729562789534
medv,gaussprLinear,no,0.164999999999992,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,6,2.81587313141561
medv,glm,no,0.00300000000000011,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,7,0.05119769329847
medv,lmStepAIC,no,0.00300000000000011,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,8,0.05119769329847
medv,glmStepAIC,no,0.00300000000000011,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,9,0.05119769329847
medv,leapBackward,no,0.0490000000000066,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,10,0.836228990541757
medv,leapForward,no,0.144999999999996,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,11,2.47455517609255
medv,leapSeq,no,0.046999999999997,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,12,0.802097195009282
medv,treebag,no,0.0510000000000019,Bagged CART,5.32464612728951,4309.48216989059,484.310059153108,3.18625038916519,0.582725226886745,26.7140574480307,65.6466462958359,13,0.87036078607399
medv,lda,no,0,failed @ fitting,,,,,,,,14,
medv,ppr,no,0.00100000000020373,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,15,0.0182663973456695
medv,qrnn,no,0.00100000000020373,Quantile Regression Neural Network,7.17422431048775,7823.3631574934,578.420788992383,3.80539992758146,0.472378412159476,52.0611110587294,88.4497776000223,16,0.0182663973456695
medv,rbf,no,0.0389999999997599,Radial Basis Function Network,9.6494075501877,14152.8820425821,1004.32082099915,6.6073738223628,114.678942264596,27.8227481842041,118.965886045463,17,0.712389496331593
medv,ppr,no,0.00300000000000011,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,15,0.05119769329847
medv,qrnn,no,0.00200000000000955,Quantile Regression Neural Network,7.17422431048775,7823.3631574934,578.420788992383,3.80539992758146,0.472378412159476,52.0611110587294,88.4497776000223,16,0.034131795532475
medv,rbf,no,0.032999999999987,Radial Basis Function Network,9.6494075501877,14152.8820425821,1004.32082099915,6.6073738223628,114.678942264596,27.8227481842041,118.965886045463,17,0.563174626282927
medv,Boruta,no,0,failed @ fitting,,,,,,,,18,
medv,qrf,no,0.0450000000000728,Quantile Random Forest,6.93722002754175,7315.0033,714.33,4.69953947368421,1.15220325143502,35,85.5277925589104,19,0.821987880388997
medv,parRF,no,0.0100000000002183,Parallel Random Forest,4.2056733307072,2688.52860102252,404.40373015873,2.66055085630744,0.50850709606605,18.8418518518519,51.8510231434493,20,0.182663973423469
medv,qrf,no,0.231999999999999,Quantile Random Forest,6.93722002754175,7315.0033,714.33,4.69953947368421,1.15220325143502,35,85.5277925589104,19,3.95928828174818
medv,parRF,no,0.00999999999999091,Parallel Random Forest,4.2056733307072,2688.52860102252,404.40373015873,2.66055085630744,0.50850709606605,18.8418518518519,51.8510231434493,20,0.170658977661405
medv,Rborist,no,0,failed @ fitting,,,,,,,,21,
medv,cforest,no,0.367000000000189,Conditional Inference Random Forest,4.94714751835186,3720.08882238691,456.891999380059,3.00586841697407,0.550092577875276,27.3468572383818,60.9925308737628,22,6.70376782449844
medv,cforest,no,0.340000000000003,Conditional Inference Random Forest,4.94714751835186,3720.08882238691,456.891999380059,3.00586841697407,0.550092577875276,27.3468572383818,60.9925308737628,22,5.8024052404931
medv,blackboost,no,0,failed @ fitting,,,,,,,,23,
medv,kernelpls,no,0.00199999999949796,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,24,0.0365327946747259
medv,pcr,no,0.00199999999949796,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,25,0.0365327946747259
medv,pls,no,0.00200000000040745,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,26,0.0365327946913391
medv,simpls,no,0.00299999999970169,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,27,0.0547991920203954
medv,kernelpls,no,0.00999999999999091,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,24,0.170658977661405
medv,pcr,no,0.0589999999999975,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,25,1.00688796820316
medv,pls,no,0.00100000000000477,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,26,0.0170658977662375
medv,simpls,no,0.00199999999998113,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,27,0.03413179553199
medv,enpls,no,0,failed @ fitting,,,,,,,,28,
medv,icr,no,0.00800000000072032,Independent Component Regression,5.42386819205533,4471.58861704801,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191595,66.8699380667278,29,0.146131178748743
medv,lars,no,0.0219999999999345,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,30,0.401860741521664
medv,lars2,no,0.0230000000001382,Least Angle Regression,5.65984499654889,4869.14449851384,560.630780424767,3.68836039753136,0.740083700026879,29.5473050114265,69.7792555027197,31,0.420127138867333
medv,lasso,no,0.00100000000020373,The lasso,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211513,28.2014531869601,66.8765583494657,32,0.0182663973456695
medv,blassoAveraged,no,0.0069999999996071,Bayesian Ridge Regression (Model Averaged),5.59580265085676,4759.577110715,557.420621417594,3.66724093037891,0.726653108311826,29.0431597589406,68.9896884375846,33,0.12786478138646
medv,BstLm,no,0.0460000000002765,Boosted Linear Model,7.40741368595203,8340.20618225407,745.622529134577,4.90541137588537,1.99558387002501,28.926648451297,91.3247293029334,34,0.840254277734667
medv,enet,no,0.00200000000040745,Elasticnet,9.56389577167804,13903.1515543912,994.230381087439,6.54098934925947,62.6152764991227,27.8471271518628,117.911626035736,35,0.0365327946913391
medv,brnn,no,0.00299999999970169,Bayesian Regularized Neural Networks,4.88891107380449,3633.02062611037,406.37218748778,2.67350123347224,0.433403954187694,33.1166340276008,60.274543765261,36,0.0547991920203954
medv,dnn,no,0.0180000000000291,Stacked AutoEncoder Deep Neural Network,1.13694499084411e+24,1.96481874655239e+50,1.72815638608305e+26,1.13694499084411e+24,Inf,1.13694499084411e+24,1.40171992443298e+25,37,
medv,elm,no,0.00199999999949796,Extreme Learning Machine,93147.8600208827,1318831621623.43,1151152.00672875,7573.36846532074,0.497009649838165,1148403.4100618,1148403.94531865,38,0.0365327946747259
medv,bagEarth,no,0.148999999999432,Bagged MARS,4.42133033841685,2971.3206181336,385.1741639435,2.53404055225987,0.460716599854261,30.6742813812277,54.5098212997768,39,2.72169320393991
medv,icr,no,0.158000000000015,Independent Component Regression,5.42386819205533,4471.58861704801,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191595,66.8699380667278,29,2.69641184705291
medv,lars,no,0.0229999999999961,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,30,0.392515648621522
medv,lars2,no,0.070999999999998,Least Angle Regression,5.65984499654889,4869.14449851384,560.630780424767,3.68836039753136,0.740083700026879,29.5473050114265,69.7792555027197,31,1.21167874139704
medv,lasso,no,0.00200000000000955,The lasso,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211513,28.2014531869601,66.8765583494657,32,0.034131795532475
medv,blassoAveraged,no,0.00699999999997658,Bayesian Ridge Regression (Model Averaged),5.59580265085676,4759.577110715,557.420621417594,3.66724093037891,0.726653108311826,29.0431597589406,68.9896884375846,33,0.119461284362692
medv,BstLm,no,0.0440000000000111,Boosted Linear Model,7.40741368595203,8340.20618225407,745.622529134577,4.90541137588537,1.99558387002501,28.926648451297,91.3247293029334,34,0.750899501711055
medv,enet,no,0.0519999999999925,Elasticnet,9.56389577167804,13903.1515543912,994.230381087439,6.54098934925947,62.6152764991227,27.8471271518628,117.911626035736,35,0.887426683839984
medv,brnn,no,0.00300000000001432,Bayesian Regularized Neural Networks,4.88891107380449,3633.02062611037,406.37218748778,2.67350123347224,0.433403954187694,33.1166340276008,60.274543765261,36,0.0511976932987125
medv,dnn,no,0.0180000000000007,Stacked AutoEncoder Deep Neural Network,1.13694499084411e+24,1.96481874655239e+50,1.72815638608305e+26,1.13694499084411e+24,Inf,1.13694499084411e+24,1.40171992443298e+25,37,
medv,elm,no,0.00200000000000955,Extreme Learning Machine,93147.8600208827,1318831621623.43,1151152.00672875,7573.36846532074,0.497009649838165,1148403.4100618,1148403.94531865,38,0.034131795532475
medv,bagEarth,no,0.154999999999973,Bagged MARS,4.42133033841685,2971.3206181336,385.1741639435,2.53404055225987,0.460716599854261,30.6742813812277,54.5098212997768,39,2.64521415375372
medv,bagEarthGCV,no,0,failed @ fitting,,,,,,,,40,
medv,deepboost,no,0,failed @ fitting,,,,,,,,41,
medv,gbm,no,0.0709999999999127,Stochastic Gradient Boosting,9.05373036409738,12459.4450928784,943.59090050644,6.20783487175289,12.019005713922,27.8543944929699,111.621884471095,42,1.29691421127673
medv,xgbTree,no,0.0700000000006185,eXtreme Gradient Boosting,4.43761344312877,2993.24678673685,450.625197792053,2.96463945915825,0.488554376152631,21.527417755127,54.7105728971728,43,1.27864781394767
medv,xgbLinear,no,0.0140000000001237,eXtreme Gradient Boosting,4.32004524774586,2836.74422327088,397.566186237335,2.61556701471931,0.444300454899162,24.045671081543,53.2610948373283,44,0.255729562789534
medv,ctree,no,0.00399999999990541,Conditional Inference Tree,5.7869963210704,5090.37761585252,576.994607298741,3.79601715328119,0.650462173826454,30.1194029850746,71.3468823134727,45,0.0730655893660649
medv,gbm,no,0.0780000000000314,Stochastic Gradient Boosting,9.05373036409738,12459.4450928784,943.59090050644,6.20783487175289,12.019005713922,27.8543944929699,111.621884471095,42,1.3311400257607
medv,xgbTree,no,0.0889999999999986,eXtreme Gradient Boosting,4.43761344312877,2993.24678673685,450.625197792053,2.96463945915825,0.488554376152631,21.527417755127,54.7105728971728,43,1.51886490118786
medv,xgbLinear,no,0.0339999999999918,eXtreme Gradient Boosting,4.32004524774586,2836.74422327088,397.566186237335,2.61556701471931,0.444300454899162,24.045671081543,53.2610948373283,44,0.580240524049165
medv,ctree,no,0.00499999999999545,Conditional Inference Tree,5.7869963210704,5090.37761585252,576.994607298741,3.79601715328119,0.650462173826454,30.1194029850746,71.3468823134727,45,0.0853294888307024
medv,ctree2,no,0,failed @ fitting,,,,,,,,46,
medv,widekernelpls,no,0.00299999999970169,Partial Least Squares,5.42387142216707,4471.5939430385,543.625216846548,3.57648168977992,0.682834030727267,28.1968977365301,66.8699778902199,47,0.0547991920203954
medv,rvmLinear,no,0.00300000000061118,Relevance Vector Machines with Linear Kernel,5.72039040280196,4973.87568679125,539.415534500556,3.54878641118787,0.626011610536545,31.717823485665,70.5257094029635,48,0.0547991920370086
medv,cubist,no,0.032999999999447,Cubist,4.12749345117433,2589.50273280202,359.217668533325,2.36327413508767,0.411597607928816,27.3164596557617,50.8871568551636,49,0.602791112274189
medv,svmRadialCost,no,0.0399999999999636,Support Vector Machines with Radial Basis Function Kernel,9.92007992446594,14958.0138275844,1021.01436598653,6.71719977622715,705.342645847963,29.138267898898,122.302959193899,50,0.730655893677263
medv,ridge,no,0.00200000000040745,Ridge Regression,5.42389456711547,4471.63210582808,543.625525495093,3.57648372036245,0.682835309097343,28.1971326887299,66.870263240308,51,0.0365327946913391
medv,foba,no,0.0219999999999345,Ridge Regression with Variable Selection,5.53702571363906,4660.11537053201,547.605453422261,3.6026674567254,0.719301706176578,29.3148950750936,68.2650376879118,52,0.401860741521664
medv,bridge,no,0.00700000000051659,Bayesian Ridge Regression,5.58362650177137,4738.88650651511,557.52780285913,3.66794607144164,0.726795712864124,28.9963823905301,68.8395707897363,53,0.127864781403074
medv,rpart1SE,no,0.00299999999970169,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,54,0.0547991920203954
medv,rpart2,no,0.0430000000005748,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,55,0.785455085714271
medv,widekernelpls,no,0.0029999999999859,Partial Least Squares,5.42387142216707,4471.5939430385,543.625216846548,3.57648168977992,0.682834030727267,28.1968977365301,66.8699778902199,47,0.0511976932982275
medv,rvmLinear,no,0.0029999999999859,Relevance Vector Machines with Linear Kernel,5.72039040280196,4973.87568679125,539.415534500556,3.54878641118787,0.626011610536545,31.717823485665,70.5257094029635,48,0.0511976932982275
medv,cubist,no,0.0540000000000305,Cubist,4.12749345117433,2589.50273280202,359.217668533325,2.36327413508767,0.411597607928816,27.3164596557617,50.8871568551636,49,0.921558479372944
medv,svmRadialCost,no,0.389999999999986,Support Vector Machines with Radial Basis Function Kernel,9.92007992446594,14958.0138275844,1021.01436598653,6.71719977622715,705.342645847963,29.138267898898,122.302959193899,50,6.65570012880061
medv,ridge,no,0.000999999999976353,Ridge Regression,5.42389456711547,4471.63210582808,543.625525495093,3.57648372036245,0.682835309097343,28.1971326887299,66.870263240308,51,0.0170658977657525
medv,foba,no,0.0199999999999818,Ridge Regression with Variable Selection,5.53702571363906,4660.11537053201,547.605453422261,3.6026674567254,0.719301706176578,29.3148950750936,68.2650376879118,52,0.34131795532281
medv,bridge,no,0.00600000000002865,Bayesian Ridge Regression,5.58362650177137,4738.88650651511,557.52780285913,3.66794607144164,0.726795712864124,28.9963823905301,68.8395707897363,53,0.102395386597425
medv,rpart1SE,no,0.00399999999996226,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,54,0.0682635910639799
medv,rpart2,no,0.0369999999999777,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,55,0.631438217347392
medv,rpart,no,0,failed @ fitting,,,,,,,,56,
medv,Linear,no,0,failed @ fitting,,,,,,,,57,
medv,Linear,no,0.000999999999976353,failed @ fitting,,,,,,,,57,
medv,Additive,no,0,failed @ fitting,,,,,,,,58,
medv,MARS,no,0.00200000000040745,MARS,4.24048615396848,2733.22186894376,399.295569620782,2.62694453697883,0.485715372253715,29.1285530431765,52.2802244538387,59,0.0365327946913391
medv,MARS3,no,0.00100000000020373,MARS,4.02454290381192,2461.93572886268,415.351393807852,2.73257495926219,0.452225855546156,20.2485432459472,49.6178972636153,60,0.0182663973456695
medv,PolyMARS,no,0.0119999999997162,PolyMARS,4.14534670775487,2611.95269777911,398.486405731937,2.62162109034169,0.467550714965535,25.2623861205008,51.107266584891,61,0.219196768098195
medv,PolyMARS3,no,0.00799999999981083,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,62,0.14613117873213
medv,MARS,no,0.0249999999999773,MARS,4.24048615396848,2733.22186894376,399.295569620782,2.62694453697883,0.485715372253715,29.1285530431765,52.2802244538387,59,0.426647444153512
medv,MARS3,no,0.0010000000000332,MARS,4.02454290381192,2461.93572886268,415.351393807852,2.73257495926219,0.452225855546156,20.2485432459472,49.6178972636153,60,0.0170658977667225
medv,PolyMARS,no,0.00499999999999545,PolyMARS,4.14534670775487,2611.95269777911,398.486405731937,2.62162109034169,0.467550714965535,25.2623861205008,51.107266584891,61,0.0853294888307024
medv,PolyMARS3,no,0.00999999999999091,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,62,0.170658977661405
medv,StepLinear,no,0,failed @ fitting,,,,,,,,63,
medv,PolyMARS_GCV1,no,0.00800000000072032,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,64,0.146131178748743
medv,mda_p01,pca,0.00199999999949796,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,65,0.0365327946747259
medv,polymars_p01,pca,0.00799999999981083,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,66,0.14613117873213
medv,rpart_anova_direct,pca,0.00300000000061118,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,67,0.0547991920370086
medv,bayesglm,pca,0.00299999999970169,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,68,0.0547991920203954
medv,rlm,pca,0.00100000000020373,failed @ fitting,,,,,,,,69,
medv,gaussprLinear,pca,0.00100000000020373,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,70,0.0182663973456695
medv,glm,pca,0.00199999999949796,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,71,0.0365327946747259
medv,lmStepAIC,pca,0.00199999999949796,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,72,0.0365327946747259
medv,glmStepAIC,pca,0.00200000000040745,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,73,0.0365327946913391
medv,leapBackward,pca,0.0500000000001819,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,74,0.913319867100732
medv,leapForward,pca,0.0509999999994761,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,75,0.931586264429788
medv,leapSeq,pca,0.0520000000005894,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,76,0.949852661792071
medv,treebag,pca,0.0140000000001237,Bagged CART,5.26126336391716,4207.49561204353,477.402976119863,3.14080905342015,0.580921164402193,27.3921057223529,64.8652111076772,77,0.255729562789534
medv,PolyMARS_GCV1,no,0.00799999999998136,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,64,0.13652718212893
medv,mda_p01,pca,0.000999999999976353,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,65,0.0170658977657525
medv,polymars_p01,pca,0.00900000000001455,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,66,0.153593079895652
medv,rpart_anova_direct,pca,0.00200000000000955,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,67,0.034131795532475
medv,bayesglm,pca,0.143000000000029,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,68,2.44042338056081
medv,rlm,pca,0,failed @ fitting,,,,,,,,69,
medv,gaussprLinear,pca,0.00199999999995271,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,70,0.0341317955315049
medv,glm,pca,0.00300000000004275,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,71,0.0511976932991975
medv,lmStepAIC,pca,0.00300000000004275,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,72,0.0511976932991975
medv,glmStepAIC,pca,0.00199999999995271,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,73,0.0341317955315049
medv,leapBackward,pca,0.0489999999999782,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,74,0.836228990541272
medv,leapForward,pca,0.0519999999999641,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,75,0.8874266838395
medv,leapSeq,pca,0.0480000000000018,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,76,0.81916309277552
medv,treebag,pca,0.0679999999999836,Bagged CART,5.26126336391716,4207.49561204353,477.402976119863,3.14080905342015,0.580921164402193,27.3921057223529,64.8652111076772,77,1.16048104809833
medv,lda,pca,0,failed @ fitting,,,,,,,,78,
medv,ppr,pca,0.00199999999949796,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,79,0.0365327946747259
medv,qrnn,pca,0.00100000000020373,Quantile Regression Neural Network,6.75771894660571,6941.3483349197,605.712472880104,3.98495047947437,0.547064818730159,40.7517081975006,83.31475460517,80,0.0182663973456695
medv,rbf,pca,0.0160000000005311,Radial Basis Function Network,9.64740807327522,14147.017344909,1004.1366569519,6.60616221678884,105.52554780645,27.8123817443848,118.941234838507,81,0.292262357480873
medv,Boruta,pca,0,failed @ fitting,,,,,,,,82,
medv,qrf,pca,0.0469999999995707,Quantile Random Forest,6.80907569660562,7047.2538,703.22,4.62644736842105,1.04336213727161,35,83.9479231428628,83,0.858520675063723
medv,parRF,pca,0.011000000000422,Parallel Random Forest,4.19809950333119,2678.85399486017,406.71962962963,2.67578703703704,0.506330793677043,19.9656878306879,51.7576467283838,84,0.200930370769139
medv,ppr,pca,0.00200000000000955,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,79,0.034131795532475
medv,qrnn,pca,0.0329999999999586,Quantile Regression Neural Network,6.75771894660571,6941.3483349197,605.712472880104,3.98495047947437,0.547064818730159,40.7517081975006,83.31475460517,80,0.563174626282442
medv,rbf,pca,0.0170000000000528,Radial Basis Function Network,9.64740807327522,14147.017344909,1004.1366569519,6.60616221678884,105.52554780645,27.8123817443848,118.941234838507,81,0.290120262025552
medv,Boruta,pca,0.0010000000000332,failed @ fitting,,,,,,,,82,
medv,qrf,pca,0.0450000000000159,Quantile Random Forest,6.80907569660562,7047.2538,703.22,4.62644736842105,1.04336213727161,35,83.9479231428628,83,0.767965399477292
medv,parRF,pca,0.0120000000000005,Parallel Random Forest,4.19809950333119,2678.85399486017,406.71962962963,2.67578703703704,0.506330793677043,19.9656878306879,51.7576467283838,84,0.20479077319388
medv,Rborist,pca,0,failed @ fitting,,,,,,,,85,
medv,cforest,pca,0.407999999999447,Conditional Inference Random Forest,4.93144096328193,3696.50471609892,452.899974790645,2.97960509730688,0.547403619496415,27.0067770829813,60.7988874577399,86,7.45269011550476
medv,cforest,pca,0.33099999999996,Conditional Inference Random Forest,4.93144096328193,3696.50471609892,452.899974790645,2.97960509730688,0.547403619496415,27.0067770829813,60.7988874577399,86,5.64881216059696
medv,blackboost,pca,0,failed @ fitting,,,,,,,,87,
medv,kernelpls,pca,0.00199999999949796,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,88,0.0365327946747259
medv,pcr,pca,0.00200000000040745,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,89,0.0365327946913391
medv,pls,pca,0.00200000000040745,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,90,0.0365327946913391
medv,simpls,pca,0.00300000000061118,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,91,0.0547991920370086
medv,kernelpls,pca,0.00200000000000955,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,88,0.034131795532475
medv,pcr,pca,0.0010000000000332,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,89,0.0170658977667225
medv,pls,pca,0.00200000000000955,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,90,0.034131795532475
medv,simpls,pca,0.00200000000000955,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,91,0.034131795532475
medv,enpls,pca,0,failed @ fitting,,,,,,,,92,
medv,icr,pca,0.00799999999981083,Independent Component Regression,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667278,93,0.14613117873213
medv,lars,pca,0.0230000000001382,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,94,0.420127138867333
medv,lars2,pca,0.0219999999999345,Least Angle Regression,5.65984499654889,4869.14449851384,560.630780424767,3.68836039753136,0.740083700026879,29.5473050114265,69.7792555027197,95,0.401860741521664
medv,lasso,pca,0.00200000000040745,The lasso,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211513,28.2014531869601,66.8765583494657,96,0.0365327946913391
medv,blassoAveraged,pca,0.00700000000051659,Bayesian Ridge Regression (Model Averaged),5.60108880613655,4768.57376376268,557.788553489951,3.6696615361181,0.726584234767487,29.0836374031592,69.0548605368419,97,0.127864781403074
medv,BstLm,pca,0.0469999999995707,Boosted Linear Model,7.40741368595203,8340.20618225407,745.622529134577,4.90541137588537,1.99558387002501,28.926648451297,91.3247293029334,98,0.858520675063723
medv,enet,pca,0.00199999999949796,Elasticnet,9.56389577167804,13903.1515543912,994.230381087439,6.54098934925947,62.6152764991227,27.8471271518628,117.911626035736,99,0.0365327946747259
medv,brnn,pca,0.00199999999949796,Bayesian Regularized Neural Networks,4.84872146835135,3573.53518140299,432.793919061992,2.84732841488152,0.42009443548083,31.1258878708199,59.7790530320027,100,0.0365327946747259
medv,dnn,pca,0.0180000000000291,Stacked AutoEncoder Deep Neural Network,4.55531315866381e+24,3.15413345197134e+51,6.92407600116899e+26,4.55531315866381e+24,Inf,4.55531315866381e+24,5.6161672446352e+25,101,
medv,elm,pca,0.00199999999949796,Extreme Learning Machine,11.3391919226264,19543.7455656398,982.155564393773,6.46154976574851,1.1321723006774,90.0596591965684,139.798946940382,102,0.0365327946747259
medv,bagEarth,pca,0.149000000000342,Bagged MARS,4.24696427315202,2741.57924168931,384.995420784734,2.53286461042588,0.466537537404775,26.9525461410482,52.3600920710545,103,2.72169320395653
medv,bagEarthGCV,pca,0,failed @ fitting,,,,,,,,104,
medv,deepboost,pca,0,failed @ fitting,,,,,,,,105,
medv,gbm,pca,0.0720000000001164,Stochastic Gradient Boosting,9.05461122750174,12461.8696411425,943.657545544844,6.20827332595292,12.0850064919185,27.8553528307427,111.632744484504,106,1.3151806086224
medv,xgbTree,pca,0.0730000000003201,eXtreme Gradient Boosting,4.24709368118252,2741.74631998456,429.884105587006,2.82818490517767,0.470560031056237,20.011060333252,52.3616875204052,107,1.33344700596807
medv,xgbLinear,pca,0.0140000000001237,eXtreme Gradient Boosting,4.32004524774586,2836.74422327088,397.566186237335,2.61556701471931,0.444300454899162,24.045671081543,53.2610948373283,108,0.255729562789534
medv,ctree,pca,0.00499999999919965,Conditional Inference Tree,5.7869963210704,5090.37761585252,576.994607298741,3.79601715328119,0.650462173826454,30.1194029850746,71.3468823134727,109,0.0913319866951213
medv,icr,pca,0.0080000000000382,Independent Component Regression,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667278,93,0.1365271821299
medv,lars,pca,0.0209999999999582,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,94,0.358383853088562
medv,lars2,pca,0.0200000000000387,Least Angle Regression,5.65984499654889,4869.14449851384,560.630780424767,3.68836039753136,0.740083700026879,29.5473050114265,69.7792555027197,95,0.34131795532378
medv,lasso,pca,0.00199999999995271,The lasso,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211513,28.2014531869601,66.8765583494657,96,0.0341317955315049
medv,blassoAveraged,pca,0.007000000000005,Bayesian Ridge Regression (Model Averaged),5.60108880613655,4768.57376376268,557.788553489951,3.6696615361181,0.726584234767487,29.0836374031592,69.0548605368419,97,0.119461284363177
medv,BstLm,pca,0.109000000000037,Boosted Linear Model,7.40741368595203,8340.20618225407,745.622529134577,4.90541137588537,1.99558387002501,28.926648451297,91.3247293029334,98,1.86018285651164
medv,enet,pca,0.000999999999976353,Elasticnet,9.56389577167804,13903.1515543912,994.230381087439,6.54098934925947,62.6152764991227,27.8471271518628,117.911626035736,99,0.0170658977657525
medv,brnn,pca,0.0029999999999859,Bayesian Regularized Neural Networks,4.84872146835135,3573.53518140299,432.793919061992,2.84732841488152,0.42009443548083,31.1258878708199,59.7790530320027,100,0.0511976932982275
medv,dnn,pca,0.0380000000000109,Stacked AutoEncoder Deep Neural Network,4.55531315866381e+24,3.15413345197134e+51,6.92407600116899e+26,4.55531315866381e+24,Inf,4.55531315866381e+24,5.6161672446352e+25,101,
medv,elm,pca,0.0010000000000332,Extreme Learning Machine,11.3391919226264,19543.7455656398,982.155564393773,6.46154976574851,1.1321723006774,90.0596591965684,139.798946940382,102,0.0170658977667225
medv,bagEarth,pca,0.178999999999974,Bagged MARS,4.24696427315202,2741.57924168931,384.995420784734,2.53286461042588,0.466537537404775,26.9525461410482,52.3600920710545,103,3.05479570014148
medv,bagEarthGCV,pca,0.0010000000000332,failed @ fitting,,,,,,,,104,
medv,deepboost,pca,0.0010000000000332,failed @ fitting,,,,,,,,105,
medv,gbm,pca,0.137,Stochastic Gradient Boosting,9.05461122750174,12461.8696411425,943.657545544844,6.20827332595292,12.0850064919185,27.8553528307427,111.632744484504,106,2.33802799396338
medv,xgbTree,pca,0.0659999999999741,eXtreme Gradient Boosting,4.24709368118252,2741.74631998456,429.884105587006,2.82818490517767,0.470560031056237,20.011060333252,52.3616875204052,107,1.12634925256585
medv,xgbLinear,pca,0.0120000000000005,eXtreme Gradient Boosting,4.32004524774586,2836.74422327088,397.566186237335,2.61556701471931,0.444300454899162,24.045671081543,53.2610948373283,108,0.20479077319388
medv,ctree,pca,0.00499999999999545,Conditional Inference Tree,5.7869963210704,5090.37761585252,576.994607298741,3.79601715328119,0.650462173826454,30.1194029850746,71.3468823134727,109,0.0853294888307024
medv,ctree2,pca,0,failed @ fitting,,,,,,,,110,
medv,widekernelpls,pca,0.00200000000040745,Partial Least Squares,5.42387142216707,4471.5939430385,543.625216846548,3.57648168977992,0.682834030727267,28.1968977365301,66.8699778902199,111,0.0365327946913391
medv,rvmLinear,pca,0.0320000000001528,Relevance Vector Machines with Linear Kernel,5.72039040280196,4973.87568679125,539.415534500556,3.54878641118787,0.626011610536545,31.717823485665,70.5257094029635,112,0.584524714945133
medv,cubist,pca,0.0320000000001528,Cubist,4.12749345117433,2589.50273280202,359.217668533325,2.36327413508767,0.411597607928816,27.3164596557617,50.8871568551636,113,0.584524714945133
medv,svmRadialCost,pca,0.0369999999993524,Support Vector Machines with Radial Basis Function Kernel,9.91955791122855,14956.439631441,1020.91644541136,6.71655556191686,686.729349462751,29.1402570362835,122.296523382478,114,0.675856701640254
medv,ridge,pca,0.00200000000040745,Ridge Regression,5.42389456711547,4471.63210582808,543.625525495093,3.57648372036245,0.682835309097343,28.1971326887299,66.870263240308,115,0.0365327946913391
medv,foba,pca,0.0199999999995271,Ridge Regression with Variable Selection,5.53702571363906,4660.11537053201,547.605453422261,3.6026674567254,0.719301706176578,29.3148950750936,68.2650376879118,116,0.365327946830325
medv,bridge,pca,0.00700000000051659,Bayesian Ridge Regression,5.5822777909574,4736.59745098327,556.838413933696,3.66341061798484,0.725682914665476,29.0049878442165,68.8229427660811,117,0.127864781403074
medv,rpart1SE,pca,0.00299999999970169,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,118,0.0547991920203954
medv,rpart2,pca,0.0420000000003711,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,119,0.767188688368602
medv,widekernelpls,pca,0.00200000000000955,Partial Least Squares,5.42387142216707,4471.5939430385,543.625216846548,3.57648168977992,0.682834030727267,28.1968977365301,66.8699778902199,111,0.034131795532475
medv,rvmLinear,pca,0.0260000000000105,Relevance Vector Machines with Linear Kernel,5.72039040280196,4973.87568679125,539.415534500556,3.54878641118787,0.626011610536545,31.717823485665,70.5257094029635,112,0.443713341920235
medv,cubist,pca,0.0489999999999782,Cubist,4.12749345117433,2589.50273280202,359.217668533325,2.36327413508767,0.411597607928816,27.3164596557617,50.8871568551636,113,0.836228990541272
medv,svmRadialCost,pca,0.0330000000000155,Support Vector Machines with Radial Basis Function Kernel,9.91955791122855,14956.439631441,1020.91644541136,6.71655556191686,686.729349462751,29.1402570362835,122.296523382478,114,0.563174626283412
medv,ridge,pca,0.000999999999976353,Ridge Regression,5.42389456711547,4471.63210582808,543.625525495093,3.57648372036245,0.682835309097343,28.1971326887299,66.870263240308,115,0.0170658977657525
medv,foba,pca,0.0470000000000255,Ridge Regression with Variable Selection,5.53702571363906,4660.11537053201,547.605453422261,3.6026674567254,0.719301706176578,29.3148950750936,68.2650376879118,116,0.802097195009767
medv,bridge,pca,0.007000000000005,Bayesian Ridge Regression,5.5822777909574,4736.59745098327,556.838413933696,3.66341061798484,0.725682914665476,29.0049878442165,68.8229427660811,117,0.119461284363177
medv,rpart1SE,pca,0.00200000000000955,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,118,0.034131795532475
medv,rpart2,pca,0.0450000000000159,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,119,0.767965399477292
medv,rpart,pca,0,failed @ fitting,,,,,,,,120,
medv,Linear,pca,0.00100000000020373,failed @ fitting,,,,,,,,121,
medv,Linear,pca,0,failed @ fitting,,,,,,,,121,
medv,Additive,pca,0,failed @ fitting,,,,,,,,122,
medv,MARS,pca,0.00200000000040745,MARS,4.24048615396848,2733.22186894376,399.295569620782,2.62694453697883,0.485715372253715,29.1285530431765,52.2802244538387,123,0.0365327946913391
medv,MARS3,pca,0.00200000000040745,MARS,4.02454290381192,2461.93572886268,415.351393807852,2.73257495926219,0.452225855546156,20.2485432459472,49.6178972636153,124,0.0365327946913391
medv,PolyMARS,pca,0.00399999999990541,PolyMARS,4.14534670775487,2611.95269777911,398.486405731937,2.62162109034169,0.467550714965535,25.2623861205008,51.107266584891,125,0.0730655893660649
medv,PolyMARS3,pca,0.00799999999981083,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,126,0.14613117873213
medv,MARS,pca,0.00200000000006639,MARS,4.24048615396848,2733.22186894376,399.295569620782,2.62694453697883,0.485715372253715,29.1285530431765,52.2802244538387,123,0.0341317955334451
medv,MARS3,pca,0.000999999999976353,MARS,4.02454290381192,2461.93572886268,415.351393807852,2.73257495926219,0.452225855546156,20.2485432459472,49.6178972636153,124,0.0170658977657525
medv,PolyMARS,pca,0.00399999999990541,PolyMARS,4.14534670775487,2611.95269777911,398.486405731937,2.62162109034169,0.467550714965535,25.2623861205008,51.107266584891,125,0.0682635910630098
medv,PolyMARS3,pca,0.0080000000000382,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,126,0.1365271821299
medv,StepLinear,pca,0,failed @ fitting,,,,,,,,127,
medv,PolyMARS_GCV1,pca,0.00799999999981083,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,128,0.14613117873213
medv,mda_p01,BoxCox,0.00100000000020373,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,129,0.0182663973456695
medv,polymars_p01,BoxCox,0.00799999999981083,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,130,0.14613117873213
medv,rpart_anova_direct,BoxCox,0.00299999999970169,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,131,0.0547991920203954
medv,bayesglm,BoxCox,0.00200000000040745,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,132,0.0365327946913391
medv,rlm,BoxCox,0,failed @ fitting,,,,,,,,133,
medv,gaussprLinear,BoxCox,0.00200000000040745,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,134,0.0365327946913391
medv,glm,BoxCox,0.00199999999949796,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,135,0.0365327946747259
medv,lmStepAIC,BoxCox,0.00299999999970169,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,136,0.0547991920203954
medv,glmStepAIC,BoxCox,0.00199999999949796,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,137,0.0365327946747259
medv,leapBackward,BoxCox,0.0560000000004948,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,138,1.02291825115814
medv,leapForward,BoxCox,0.0619999999998981,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,139,1.13251663519893
medv,leapSeq,BoxCox,0.0600000000004002,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,140,1.0959838405242
medv,treebag,BoxCox,0.0160000000005311,Bagged CART,5.2999592158409,4269.61428881569,490.473303494008,3.22679804930268,0.583959381067449,25.6175850126859,65.3422856105883,141,0.292262357480873
medv,PolyMARS_GCV1,pca,0.00799999999992451,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,128,0.13652718212796
medv,mda_p01,BoxCox,0.00199999999995271,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,129,0.0341317955315049
medv,polymars_p01,BoxCox,0.00699999999994816,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,130,0.119461284362207
medv,rpart_anova_direct,BoxCox,0.00299999999992906,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,131,0.0511976932972574
medv,bayesglm,BoxCox,0.00199999999995271,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,132,0.0341317955315049
medv,rlm,BoxCox,0.000999999999976353,failed @ fitting,,,,,,,,133,
medv,gaussprLinear,BoxCox,0.000999999999976353,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,134,0.0170658977657525
medv,glm,BoxCox,0.00199999999995271,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,135,0.0341317955315049
medv,lmStepAIC,BoxCox,0.000999999999976353,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,136,0.0170658977657525
medv,glmStepAIC,BoxCox,0.00199999999995271,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,137,0.0341317955315049
medv,leapBackward,BoxCox,0.0499999999999545,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,138,0.853294888307024
medv,leapForward,BoxCox,0.0470000000000255,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,139,0.802097195009767
medv,leapSeq,BoxCox,0.0469999999999118,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,140,0.802097195007827
medv,treebag,BoxCox,0.01400000000001,Bagged CART,5.2999592158409,4269.61428881569,490.473303494008,3.22679804930268,0.583959381067449,25.6175850126859,65.3422856105883,141,0.238922568726355
medv,lda,BoxCox,0,failed @ fitting,,,,,,,,142,
medv,ppr,BoxCox,0.00200000000040745,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,143,0.0365327946913391
medv,qrnn,BoxCox,0.000999999999294232,Quantile Regression Neural Network,11.9262740636778,21619.8739823769,730.228291068153,4.80413349386943,0.534381940578158,102.153037545716,147.036981682762,144,0.0182663973290563
medv,rbf,BoxCox,0.0169999999998254,Radial Basis Function Network,9.64954934181884,14153.2979800299,1004.28339309692,6.60712758616397,109.817624974303,27.8199119567871,118.967634170096,145,0.310528754809929
medv,ppr,BoxCox,0.00199999999995271,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,143,0.0341317955315049
medv,qrnn,BoxCox,0.000999999999976353,Quantile Regression Neural Network,11.9262740636778,21619.8739823769,730.228291068153,4.80413349386943,0.534381940578158,102.153037545716,147.036981682762,144,0.0170658977657525
medv,rbf,BoxCox,0.0149999999999864,Radial Basis Function Network,9.64954934181884,14153.2979800299,1004.28339309692,6.60712758616397,109.817624974303,27.8199119567871,118.967634170096,145,0.255988466492107
medv,Boruta,BoxCox,0,failed @ fitting,,,,,,,,146,
medv,qrf,BoxCox,0.0450000000000728,Quantile Random Forest,6.90764669730125,7252.7686,719.62,4.73434210526316,1.1167645482156,33.9,85.1631880568124,147,0.821987880388997
medv,parRF,BoxCox,0.00999999999930878,Parallel Random Forest,4.2770839312369,2780.60393713642,403.27507936508,2.65312552213868,0.491446858893271,21.3965079365079,52.7314321551807,148,0.182663973406856
medv,qrf,BoxCox,0.0430000000000064,Quantile Random Forest,6.90764669730125,7252.7686,719.62,4.73434210526316,1.1167645482156,33.9,85.1631880568124,147,0.733833603944817
medv,parRF,BoxCox,0.00999999999999091,Parallel Random Forest,4.2770839312369,2780.60393713642,403.27507936508,2.65312552213868,0.491446858893271,21.3965079365079,52.7314321551807,148,0.170658977661405
medv,Rborist,BoxCox,0,failed @ fitting,,,,,,,,149,
medv,cforest,BoxCox,0.378999999999905,Conditional Inference Random Forest,4.94323865187207,3714.21247214303,453.166093575154,2.98135587878391,0.546237849018575,27.2009772535805,60.9443391312354,150,6.92296459259663
medv,cforest,BoxCox,0.327999999999975,Conditional Inference Random Forest,4.94323865187207,3714.21247214303,453.166093575154,2.98135587878391,0.546237849018575,27.2009772535805,60.9443391312354,150,5.59761446729874
medv,blackboost,BoxCox,0,failed @ fitting,,,,,,,,151,
medv,kernelpls,BoxCox,0.00300000000061118,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,152,0.0547991920370086
medv,pcr,BoxCox,0.00400000000081491,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,153,0.0730655893826781
medv,pls,BoxCox,0.00299999999970169,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,154,0.0547991920203954
medv,simpls,BoxCox,0.00200000000040745,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,155,0.0365327946913391
medv,kernelpls,BoxCox,0.00200000000006639,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,152,0.0341317955334451
medv,pcr,BoxCox,0.00199999999995271,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,153,0.0341317955315049
medv,pls,BoxCox,0.00199999999995271,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,154,0.0341317955315049
medv,simpls,BoxCox,0.00200000000006639,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,155,0.0341317955334451
medv,enpls,BoxCox,0,failed @ fitting,,,,,,,,156,
medv,icr,BoxCox,0.0069999999996071,Independent Component Regression,5.42386819205533,4471.58861704801,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191595,66.8699380667278,157,0.12786478138646
medv,lars,BoxCox,0.0230000000001382,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,158,0.420127138867333
medv,lars2,BoxCox,0.0280000000002474,Least Angle Regression,5.65984499654889,4869.14449851384,560.630780424767,3.68836039753136,0.740083700026879,29.5473050114265,69.7792555027197,159,0.511459125579068
medv,lasso,BoxCox,0.00199999999949796,The lasso,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211513,28.2014531869601,66.8765583494657,160,0.0365327946747259
medv,blassoAveraged,BoxCox,0.00799999999981083,Bayesian Ridge Regression (Model Averaged),5.60485132481282,4774.98247273491,558.147037388737,3.67201998282064,0.728127772022443,29.1072902784887,69.1012479824707,161,0.14613117873213
medv,BstLm,BoxCox,0.0479999999997745,Boosted Linear Model,7.40741368595203,8340.20618225407,745.622529134577,4.90541137588537,1.99558387002501,28.926648451297,91.3247293029334,162,0.876787072409393
medv,enet,BoxCox,0.00200000000040745,Elasticnet,9.56389577167804,13903.1515543912,994.230381087439,6.54098934925947,62.6152764991227,27.8471271518628,117.911626035736,163,0.0365327946913391
medv,brnn,BoxCox,0.00200000000040745,Bayesian Regularized Neural Networks,4.09188372930229,2545.01389302757,384.866928806797,2.53201926846577,0.411612792589191,23.2494281907315,50.4481307188639,164,0.0365327946913391
medv,dnn,BoxCox,0.024000000000342,Stacked AutoEncoder Deep Neural Network,7.27268035925202e+23,8.0395657003932e+49,1.10544741460631e+26,7.27268035925202e+23,Inf,7.27268035925202e+23,8.96636252913811e+24,165,
medv,elm,BoxCox,0.00199999999949796,Extreme Learning Machine,8.33814299753472,10567.7435543953,897.96568485715,5.90766897932335,1.30458854949992,32.874833860511,102.799530905521,166,0.0365327946747259
medv,bagEarth,BoxCox,0.167000000000371,Bagged MARS,4.24404620862204,2737.81308957971,389.112696825105,2.55995195279674,0.485136476995803,25.9716848473493,52.3241157553542,167,3.05048835611212
medv,icr,BoxCox,0.00599999999997181,Independent Component Regression,5.42386819205533,4471.58861704801,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191595,66.8699380667278,157,0.102395386596455
medv,lars,BoxCox,0.0190000000000055,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,158,0.324252057557057
medv,lars2,BoxCox,0.0190000000000055,Least Angle Regression,5.65984499654889,4869.14449851384,560.630780424767,3.68836039753136,0.740083700026879,29.5473050114265,69.7792555027197,159,0.324252057557057
medv,lasso,BoxCox,0.000999999999976353,The lasso,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211513,28.2014531869601,66.8765583494657,160,0.0170658977657525
medv,blassoAveraged,BoxCox,0.00600000000008549,Bayesian Ridge Regression (Model Averaged),5.60485132481282,4774.98247273491,558.147037388737,3.67201998282064,0.728127772022443,29.1072902784887,69.1012479824707,161,0.102395386598395
medv,BstLm,BoxCox,0.0529999999999973,Boosted Linear Model,7.40741368595203,8340.20618225407,745.622529134577,4.90541137588537,1.99558387002501,28.926648451297,91.3247293029334,162,0.904492581606222
medv,enet,BoxCox,0.00100000000009004,Elasticnet,9.56389577167804,13903.1515543912,994.230381087439,6.54098934925947,62.6152764991227,27.8471271518628,117.911626035736,163,0.0170658977676926
medv,brnn,BoxCox,0.000999999999976353,Bayesian Regularized Neural Networks,4.09188372930229,2545.01389302757,384.866928806797,2.53201926846577,0.411612792589191,23.2494281907315,50.4481307188639,164,0.0170658977657525
medv,dnn,BoxCox,0.0249999999999773,Stacked AutoEncoder Deep Neural Network,7.27268035925202e+23,8.0395657003932e+49,1.10544741460631e+26,7.27268035925202e+23,Inf,7.27268035925202e+23,8.96636252913811e+24,165,
medv,elm,BoxCox,0.000999999999976353,Extreme Learning Machine,8.33814299753472,10567.7435543953,897.96568485715,5.90766897932335,1.30458854949992,32.874833860511,102.799530905521,166,0.0170658977657525
medv,bagEarth,BoxCox,0.134999999999991,Bagged MARS,4.24404620862204,2737.81308957971,389.112696825105,2.55995195279674,0.485136476995803,25.9716848473493,52.3241157553542,167,2.30389619843091
medv,bagEarthGCV,BoxCox,0,failed @ fitting,,,,,,,,168,
medv,deepboost,BoxCox,0,failed @ fitting,,,,,,,,169,
medv,gbm,BoxCox,0.0700000000006185,Stochastic Gradient Boosting,9.05284830319193,12457.017484892,943.224042273286,6.2054213307453,12.0599759859357,27.8635982425354,111.6110096939,170,1.27864781394767
medv,xgbTree,BoxCox,0.0689999999995052,eXtreme Gradient Boosting,4.31703480851389,2832.79200976393,439.235462856293,2.88970699247561,0.485358796327627,21.6980110168457,53.2239796498151,171,1.26038141658539
medv,xgbLinear,BoxCox,0.0150000000003274,eXtreme Gradient Boosting,4.32004524774586,2836.74422327088,397.566186237335,2.61556701471931,0.444300454899162,24.045671081543,53.2610948373283,172,0.273995960135203
medv,ctree,BoxCox,0.00500000000010914,Conditional Inference Tree,5.7869963210704,5090.37761585252,576.994607298741,3.79601715328119,0.650462173826454,30.1194029850746,71.3468823134727,173,0.0913319867117345
medv,ctree2,BoxCox,0,failed @ fitting,,,,,,,,174,
medv,widekernelpls,BoxCox,0.00199999999949796,Partial Least Squares,5.42387142216707,4471.5939430385,543.625216846548,3.57648168977992,0.682834030727267,28.1968977365301,66.8699778902199,175,0.0365327946747259
medv,rvmLinear,BoxCox,0.00100000000020373,Relevance Vector Machines with Linear Kernel,5.72039040280196,4973.87568679125,539.415534500556,3.54878641118787,0.626011610536545,31.717823485665,70.5257094029635,176,0.0182663973456695
medv,cubist,BoxCox,0.0339999999996508,Cubist,4.12749345117433,2589.50273280202,359.217668533325,2.36327413508767,0.411597607928816,27.3164596557617,50.8871568551636,177,0.621057509619859
medv,svmRadialCost,BoxCox,0.0370000000002619,Support Vector Machines with Radial Basis Function Kernel,9.92085710671052,14960.3576712288,1021.1697829276,6.71822225610266,741.918723784722,29.1348852813495,122.312540940121,178,0.675856701656867
medv,ridge,BoxCox,0.00200000000040745,Ridge Regression,5.42389456711547,4471.63210582808,543.625525495093,3.57648372036245,0.682835309097343,28.1971326887299,66.870263240308,179,0.0365327946913391
medv,foba,BoxCox,0.0200000000004366,Ridge Regression with Variable Selection,5.53702571363906,4660.11537053201,547.605453422261,3.6026674567254,0.719301706176578,29.3148950750936,68.2650376879118,180,0.365327946846938
medv,bridge,BoxCox,0.00700000000051659,Bayesian Ridge Regression,5.58082900644622,4734.1391646771,557.511372391576,3.66783797626037,0.727318671543144,28.9751510925372,68.805080951025,181,0.127864781403074
medv,rpart1SE,BoxCox,0.00300000000061118,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,182,0.0547991920370086
medv,rpart2,BoxCox,0.0420000000003711,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,183,0.767188688368602
medv,gbm,BoxCox,0.0639999999999645,Stochastic Gradient Boosting,9.05284830319193,12457.017484892,943.224042273286,6.2054213307453,12.0599759859357,27.8635982425354,111.6110096939,170,1.09221745703338
medv,xgbTree,BoxCox,0.0679999999999836,eXtreme Gradient Boosting,4.31703480851389,2832.79200976393,439.235462856293,2.88970699247561,0.485358796327627,21.6980110168457,53.2239796498151,171,1.16048104809833
medv,xgbLinear,BoxCox,0.0120000000000573,eXtreme Gradient Boosting,4.32004524774586,2836.74422327088,397.566186237335,2.61556701471931,0.444300454899162,24.045671081543,53.2610948373283,172,0.20479077319485
medv,ctree,BoxCox,0.0040000000000191,Conditional Inference Tree,5.7869963210704,5090.37761585252,576.994607298741,3.79601715328119,0.650462173826454,30.1194029850746,71.3468823134727,173,0.06826359106495
medv,ctree2,BoxCox,0.00100000000009004,failed @ fitting,,,,,,,,174,
medv,widekernelpls,BoxCox,0.00200000000006639,Partial Least Squares,5.42387142216707,4471.5939430385,543.625216846548,3.57648168977992,0.682834030727267,28.1968977365301,66.8699778902199,175,0.0341317955334451
medv,rvmLinear,BoxCox,0.00200000000006639,Relevance Vector Machines with Linear Kernel,5.72039040280196,4973.87568679125,539.415534500556,3.54878641118787,0.626011610536545,31.717823485665,70.5257094029635,176,0.0341317955334451
medv,cubist,BoxCox,0.0299999999999727,Cubist,4.12749345117433,2589.50273280202,359.217668533325,2.36327413508767,0.411597607928816,27.3164596557617,50.8871568551636,177,0.511976932984215
medv,svmRadialCost,BoxCox,0.0349999999999682,Support Vector Machines with Radial Basis Function Kernel,9.92085710671052,14960.3576712288,1021.1697829276,6.71822225610266,741.918723784722,29.1348852813495,122.312540940121,178,0.597306421814917
medv,ridge,BoxCox,0.000999999999976353,Ridge Regression,5.42389456711547,4471.63210582808,543.625525495093,3.57648372036245,0.682835309097343,28.1971326887299,66.870263240308,179,0.0170658977657525
medv,foba,BoxCox,0.0199999999999818,Ridge Regression with Variable Selection,5.53702571363906,4660.11537053201,547.605453422261,3.6026674567254,0.719301706176578,29.3148950750936,68.2650376879118,180,0.34131795532281
medv,bridge,BoxCox,0.00700000000006185,Bayesian Ridge Regression,5.58082900644622,4734.1391646771,557.511372391576,3.66783797626037,0.727318671543144,28.9751510925372,68.805080951025,181,0.119461284364148
medv,rpart1SE,BoxCox,0.00200000000006639,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,182,0.0341317955334451
medv,rpart2,BoxCox,0.0399999999999636,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,183,0.68263591064562
medv,rpart,BoxCox,0,failed @ fitting,,,,,,,,184,
medv,Linear,BoxCox,0,failed @ fitting,,,,,,,,185,
medv,Additive,BoxCox,0,failed @ fitting,,,,,,,,186,
medv,MARS,BoxCox,0.00199999999949796,MARS,4.24048615396848,2733.22186894376,399.295569620782,2.62694453697883,0.485715372253715,29.1285530431765,52.2802244538387,187,0.0365327946747259
medv,MARS3,BoxCox,0.00199999999949796,MARS,4.02454290381192,2461.93572886268,415.351393807852,2.73257495926219,0.452225855546156,20.2485432459472,49.6178972636153,188,0.0365327946747259
medv,PolyMARS,BoxCox,0.00500000000010914,PolyMARS,4.14534670775487,2611.95269777911,398.486405731937,2.62162109034169,0.467550714965535,25.2623861205008,51.107266584891,189,0.0913319867117345
medv,PolyMARS3,BoxCox,0.00800000000072032,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,190,0.146131178748743
medv,MARS,BoxCox,0.000999999999976353,MARS,4.24048615396848,2733.22186894376,399.295569620782,2.62694453697883,0.485715372253715,29.1285530431765,52.2802244538387,187,0.0170658977657525
medv,MARS3,BoxCox,0.00200000000006639,MARS,4.02454290381192,2461.93572886268,415.351393807852,2.73257495926219,0.452225855546156,20.2485432459472,49.6178972636153,188,0.0341317955334451
medv,PolyMARS,BoxCox,0.00499999999999545,PolyMARS,4.14534670775487,2611.95269777911,398.486405731937,2.62162109034169,0.467550714965535,25.2623861205008,51.107266584891,189,0.0853294888307024
medv,PolyMARS3,BoxCox,0.00799999999992451,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,190,0.13652718212796
medv,StepLinear,BoxCox,0,failed @ fitting,,,,,,,,191,
medv,PolyMARS_GCV1,BoxCox,0.00900000000001455,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,192,0.164397576077799
medv,mda_p01,YeoJohnson,0.00200000000040745,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,193,0.0365327946913391
medv,polymars_p01,YeoJohnson,0.00799999999981083,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,194,0.14613117873213
medv,rpart_anova_direct,YeoJohnson,0.00299999999970169,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,195,0.0547991920203954
medv,bayesglm,YeoJohnson,0.00299999999970169,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,196,0.0547991920203954
medv,PolyMARS_GCV1,BoxCox,0.00900000000001455,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,192,0.153593079895652
medv,mda_p01,YeoJohnson,0.000999999999976353,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,193,0.0170658977657525
medv,polymars_p01,YeoJohnson,0.00900000000001455,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,194,0.153593079895652
medv,rpart_anova_direct,YeoJohnson,0.00199999999995271,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,195,0.0341317955315049
medv,bayesglm,YeoJohnson,0.00199999999995271,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,196,0.0341317955315049
medv,rlm,YeoJohnson,0,failed @ fitting,,,,,,,,197,
medv,gaussprLinear,YeoJohnson,0.00100000000020373,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,198,0.0182663973456695
medv,glm,YeoJohnson,0.00300000000061118,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,199,0.0547991920370086
medv,lmStepAIC,YeoJohnson,0.00199999999949796,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,200,0.0365327946747259
medv,glmStepAIC,YeoJohnson,0.00199999999949796,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,201,0.0365327946747259
medv,leapBackward,YeoJohnson,0.0500000000001819,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,202,0.913319867100732
medv,leapForward,YeoJohnson,0.0510000000003856,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,203,0.931586264446401
medv,leapSeq,YeoJohnson,0.0520000000005894,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,204,0.949852661792071
medv,treebag,YeoJohnson,0.0140000000001237,Bagged CART,5.23845777009544,4171.09885097914,479.133036133349,3.15219102719309,0.583580977611578,26.8794943608945,64.584044863876,205,0.255729562789534
medv,gaussprLinear,YeoJohnson,0.000999999999976353,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,198,0.0170658977657525
medv,glm,YeoJohnson,0.00200000000006639,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,199,0.0341317955334451
medv,lmStepAIC,YeoJohnson,0.00199999999995271,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,200,0.0341317955315049
medv,glmStepAIC,YeoJohnson,0.00199999999995271,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,201,0.0341317955315049
medv,leapBackward,YeoJohnson,0.0460000000000491,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,202,0.785031297244015
medv,leapForward,YeoJohnson,0.0480000000000018,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,203,0.81916309277552
medv,leapSeq,YeoJohnson,0.0489999999999782,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,204,0.836228990541272
medv,treebag,YeoJohnson,0.0159999999999627,Bagged CART,5.23845777009544,4171.09885097914,479.133036133349,3.15219102719309,0.583580977611578,26.8794943608945,64.584044863876,205,0.27305436425786
medv,lda,YeoJohnson,0,failed @ fitting,,,,,,,,206,
medv,ppr,YeoJohnson,0.00199999999949796,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,207,0.0365327946747259
medv,qrnn,YeoJohnson,0.00200000000040745,Quantile Regression Neural Network,15.676248065688,37353.2025193821,798.457856289114,5.25301221242838,0.517536681927476,155.478344597426,193.269766180285,208,0.0365327946913391
medv,rbf,YeoJohnson,0.0180000000000291,Radial Basis Function Network,9.65123151632671,14158.2330068242,1005.84936447144,6.61743002941734,170.353363051351,27.8245620727539,118.98837341028,209,0.328795152155599
medv,ppr,YeoJohnson,0.000999999999976353,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,207,0.0170658977657525
medv,qrnn,YeoJohnson,0.000999999999976353,Quantile Regression Neural Network,15.676248065688,37353.2025193821,798.457856289114,5.25301221242838,0.517536681927476,155.478344597426,193.269766180285,208,0.0170658977657525
medv,rbf,YeoJohnson,0.0149999999999864,Radial Basis Function Network,9.65123151632671,14158.2330068242,1005.84936447144,6.61743002941734,170.353363051351,27.8245620727539,118.98837341028,209,0.255988466492107
medv,Boruta,YeoJohnson,0,failed @ fitting,,,,,,,,210,
medv,qrf,YeoJohnson,0.043999999999869,Quantile Random Forest,6.8828321416023,7200.7535,707.79,4.65651315789474,1.08840823324681,35,84.8572536675563,211,0.803721483043328
medv,parRF,YeoJohnson,0.0100000000002183,Parallel Random Forest,4.11029461214932,2567.96731339689,397.813703703704,2.61719541910332,0.489880637996581,20.1908201058201,50.6751153269224,212,0.182663973423469
medv,qrf,YeoJohnson,0.0430000000000064,Quantile Random Forest,6.8828321416023,7200.7535,707.79,4.65651315789474,1.08840823324681,35,84.8572536675563,211,0.733833603944817
medv,parRF,YeoJohnson,0.00999999999999091,Parallel Random Forest,4.11029461214932,2567.96731339689,397.813703703704,2.61719541910332,0.489880637996581,20.1908201058201,50.6751153269224,212,0.170658977661405
medv,Rborist,YeoJohnson,0,failed @ fitting,,,,,,,,213,
medv,cforest,YeoJohnson,0.445000000000618,Conditional Inference Random Forest,4.95581439300686,3733.13463728596,453.71783008706,2.98498572425698,0.546240383009825,27.205475810739,61.0993832807333,214,8.12854681717824
medv,blackboost,YeoJohnson,0,failed @ fitting,,,,,,,,215,
medv,kernelpls,YeoJohnson,0.00199999999949796,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,216,0.0365327946747259
medv,pcr,YeoJohnson,0.00199999999949796,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,217,0.0365327946747259
medv,pls,YeoJohnson,0.00200000000040745,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,218,0.0365327946913391
medv,simpls,YeoJohnson,0.00199999999949796,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,219,0.0365327946747259
medv,cforest,YeoJohnson,0.331000000000017,Conditional Inference Random Forest,4.95581439300686,3733.13463728596,453.71783008706,2.98498572425698,0.546240383009825,27.205475810739,61.0993832807333,214,5.64881216059793
medv,blackboost,YeoJohnson,0.000999999999976353,failed @ fitting,,,,,,,,215,
medv,kernelpls,YeoJohnson,0.00199999999995271,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,216,0.0341317955315049
medv,pcr,YeoJohnson,0.00199999999995271,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,217,0.0341317955315049
medv,pls,YeoJohnson,0.00300000000004275,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,218,0.0511976932991975
medv,simpls,YeoJohnson,0.00200000000006639,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,219,0.0341317955334451
medv,enpls,YeoJohnson,0,failed @ fitting,,,,,,,,220,
medv,icr,YeoJohnson,0.00799999999981083,Independent Component Regression,5.42386819205533,4471.58861704801,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191595,66.8699380667278,221,0.14613117873213
medv,lars,YeoJohnson,0.0229999999992288,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,222,0.42012713885072
medv,lars2,YeoJohnson,0.0219999999999345,Least Angle Regression,5.65984499654889,4869.14449851384,560.630780424767,3.68836039753136,0.740083700026879,29.5473050114265,69.7792555027197,223,0.401860741521664
medv,lasso,YeoJohnson,0.00199999999949796,The lasso,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211513,28.2014531869601,66.8765583494657,224,0.0365327946747259
medv,blassoAveraged,YeoJohnson,0.00700000000051659,Bayesian Ridge Regression (Model Averaged),5.59960627647744,4766.04974863796,558.313029765523,3.67311203793107,0.726320775497319,29.0714574697471,69.036582683661,225,0.127864781403074
medv,BstLm,YeoJohnson,0.0469999999995707,Boosted Linear Model,7.40741368595203,8340.20618225407,745.622529134577,4.90541137588537,1.99558387002501,28.926648451297,91.3247293029334,226,0.858520675063723
medv,enet,YeoJohnson,0.00100000000020373,Elasticnet,9.56389577167804,13903.1515543912,994.230381087439,6.54098934925947,62.6152764991227,27.8471271518628,117.911626035736,227,0.0182663973456695
medv,brnn,YeoJohnson,0.00100000000020373,Bayesian Regularized Neural Networks,4.48841599915151,3062.17348357877,426.367955229844,2.80505233703844,0.426623911414918,22.4554890834066,55.3369088726392,228,0.0182663973456695
medv,dnn,YeoJohnson,0.0180000000000291,Stacked AutoEncoder Deep Neural Network,2.70867099939734e+24,1.11520858461238e+51,4.11717991908396e+26,2.70867099939734e+24,Inf,2.70867099939734e+24,3.33947388762419e+25,229,
medv,elm,YeoJohnson,0.00200000000040745,Extreme Learning Machine,240.980469586085,8826881.18173334,4291.03450863225,28.2304901883701,0.596245907686476,2946.42305754368,2971.006762317,230,0.0365327946913391
medv,bagEarth,YeoJohnson,0.147999999999229,Bagged MARS,4.26807682550064,2768.9049278331,371.957604247061,2.4470895016254,0.457144412111785,30.9825857509931,52.6203850977271,231,2.70342680659424
medv,icr,YeoJohnson,0.00700000000006185,Independent Component Regression,5.42386819205533,4471.58861704801,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191595,66.8699380667278,221,0.119461284364148
medv,lars,YeoJohnson,0.0209999999999582,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,222,0.358383853088562
medv,lars2,YeoJohnson,0.0220000000000482,Least Angle Regression,5.65984499654889,4869.14449851384,560.630780424767,3.68836039753136,0.740083700026879,29.5473050114265,69.7792555027197,223,0.375449750856255
medv,lasso,YeoJohnson,0.00200000000006639,The lasso,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211513,28.2014531869601,66.8765583494657,224,0.0341317955334451
medv,blassoAveraged,YeoJohnson,0.00699999999994816,Bayesian Ridge Regression (Model Averaged),5.59960627647744,4766.04974863796,558.313029765523,3.67311203793107,0.726320775497319,29.0714574697471,69.036582683661,225,0.119461284362207
medv,BstLm,YeoJohnson,0.0500000000000682,Boosted Linear Model,7.40741368595203,8340.20618225407,745.622529134577,4.90541137588537,1.99558387002501,28.926648451297,91.3247293029334,226,0.853294888308965
medv,enet,YeoJohnson,0.00200000000006639,Elasticnet,9.56389577167804,13903.1515543912,994.230381087439,6.54098934925947,62.6152764991227,27.8471271518628,117.911626035736,227,0.0341317955334451
medv,brnn,YeoJohnson,0.00200000000006639,Bayesian Regularized Neural Networks,4.48841599915151,3062.17348357877,426.367955229844,2.80505233703844,0.426623911414918,22.4554890834066,55.3369088726392,228,0.0341317955334451
medv,dnn,YeoJohnson,0.0169999999999391,Stacked AutoEncoder Deep Neural Network,2.70867099939734e+24,1.11520858461238e+51,4.11717991908396e+26,2.70867099939734e+24,Inf,2.70867099939734e+24,3.33947388762419e+25,229,
medv,elm,YeoJohnson,0.000999999999976353,Extreme Learning Machine,240.980469586085,8826881.18173334,4291.03450863225,28.2304901883701,0.596245907686476,2946.42305754368,2971.006762317,230,0.0170658977657525
medv,bagEarth,YeoJohnson,0.133999999999901,Bagged MARS,4.26807682550064,2768.9049278331,371.957604247061,2.4470895016254,0.457144412111785,30.9825857509931,52.6203850977271,231,2.28683030066321
medv,bagEarthGCV,YeoJohnson,0,failed @ fitting,,,,,,,,232,
medv,deepboost,YeoJohnson,0,failed @ fitting,,,,,,,,233,
medv,gbm,YeoJohnson,0.069999999999709,Stochastic Gradient Boosting,9.05393795957407,12460.0164715241,943.486527822315,6.20714820935733,12.0688642277558,27.8634157124223,111.624443880022,234,1.27864781393106
medv,xgbTree,YeoJohnson,0.0700000000006185,eXtreme Gradient Boosting,4.41946821881751,2968.81829924499,459.898674106598,3.02564917175393,0.509137759354138,19.9145294189453,54.4868635475101,235,1.27864781394767
medv,xgbLinear,YeoJohnson,0.0140000000001237,eXtreme Gradient Boosting,4.32004524774586,2836.74422327088,397.566186237335,2.61556701471931,0.444300454899162,24.045671081543,53.2610948373283,236,0.255729562789534
medv,ctree,YeoJohnson,0.00399999999990541,Conditional Inference Tree,5.7869963210704,5090.37761585252,576.994607298741,3.79601715328119,0.650462173826454,30.1194029850746,71.3468823134727,237,0.0730655893660649
medv,gbm,YeoJohnson,0.0690000000000737,Stochastic Gradient Boosting,9.05393795957407,12460.0164715241,943.486527822315,6.20714820935733,12.0688642277558,27.8634157124223,111.624443880022,234,1.17754694586602
medv,xgbTree,YeoJohnson,0.0679999999999836,eXtreme Gradient Boosting,4.41946821881751,2968.81829924499,459.898674106598,3.02564917175393,0.509137759354138,19.9145294189453,54.4868635475101,235,1.16048104809833
medv,xgbLinear,YeoJohnson,0.0130000000000337,eXtreme Gradient Boosting,4.32004524774586,2836.74422327088,397.566186237335,2.61556701471931,0.444300454899162,24.045671081543,53.2610948373283,236,0.221856670960602
medv,ctree,YeoJohnson,0.0040000000000191,Conditional Inference Tree,5.7869963210704,5090.37761585252,576.994607298741,3.79601715328119,0.650462173826454,30.1194029850746,71.3468823134727,237,0.06826359106495
medv,ctree2,YeoJohnson,0,failed @ fitting,,,,,,,,238,
medv,widekernelpls,YeoJohnson,0.00200000000040745,Partial Least Squares,5.42387142216707,4471.5939430385,543.625216846548,3.57648168977992,0.682834030727267,28.1968977365301,66.8699778902199,239,0.0365327946913391
medv,rvmLinear,YeoJohnson,0.00199999999949796,Relevance Vector Machines with Linear Kernel,5.72039040280196,4973.87568679125,539.415534500556,3.54878641118787,0.626011610536545,31.717823485665,70.5257094029635,240,0.0365327946747259
medv,cubist,YeoJohnson,0.0309999999999491,Cubist,4.12749345117433,2589.50273280202,359.217668533325,2.36327413508767,0.411597607928816,27.3164596557617,50.8871568551636,241,0.566258317599463
medv,svmRadialCost,YeoJohnson,0.0380000000004657,Support Vector Machines with Radial Basis Function Kernel,9.92093431385627,14960.5905242973,1021.18585002933,6.71832796071925,746.243549358368,29.1345218484301,122.313492813742,242,0.694123099002537
medv,ridge,YeoJohnson,0.00200000000040745,Ridge Regression,5.42389456711547,4471.63210582808,543.625525495093,3.57648372036245,0.682835309097343,28.1971326887299,66.870263240308,243,0.0365327946913391
medv,foba,YeoJohnson,0.0209999999997308,Ridge Regression with Variable Selection,5.53702571363906,4660.11537053201,547.605453422261,3.6026674567254,0.719301706176578,29.3148950750936,68.2650376879118,244,0.383594344175994
medv,bridge,YeoJohnson,0.00700000000051659,Bayesian Ridge Regression,5.58973460717912,4749.26021276178,557.738604134145,3.66933292193517,0.724470188862762,29.0610695098891,68.9148765707505,245,0.127864781403074
medv,rpart1SE,YeoJohnson,0.00299999999970169,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,246,0.0547991920203954
medv,rpart2,YeoJohnson,0.0420000000003711,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,247,0.767188688368602
medv,widekernelpls,YeoJohnson,0.00199999999995271,Partial Least Squares,5.42387142216707,4471.5939430385,543.625216846548,3.57648168977992,0.682834030727267,28.1968977365301,66.8699778902199,239,0.0341317955315049
medv,rvmLinear,YeoJohnson,0.000999999999976353,Relevance Vector Machines with Linear Kernel,5.72039040280196,4973.87568679125,539.415534500556,3.54878641118787,0.626011610536545,31.717823485665,70.5257094029635,240,0.0170658977657525
medv,cubist,YeoJohnson,0.0289999999999964,Cubist,4.12749345117433,2589.50273280202,359.217668533325,2.36327413508767,0.411597607928816,27.3164596557617,50.8871568551636,241,0.494911035218462
medv,svmRadialCost,YeoJohnson,0.0319999999999254,Support Vector Machines with Radial Basis Function Kernel,9.92093431385627,14960.5905242973,1021.18585002933,6.71832796071925,746.243549358368,29.1345218484301,122.313492813742,242,0.54610872851572
medv,ridge,YeoJohnson,0.00200000000006639,Ridge Regression,5.42389456711547,4471.63210582808,543.625525495093,3.57648372036245,0.682835309097343,28.1971326887299,66.870263240308,243,0.0341317955334451
medv,foba,YeoJohnson,0.0169999999999391,Ridge Regression with Variable Selection,5.53702571363906,4660.11537053201,547.605453422261,3.6026674567254,0.719301706176578,29.3148950750936,68.2650376879118,244,0.290120262023612
medv,bridge,YeoJohnson,0.00599999999997181,Bayesian Ridge Regression,5.58973460717912,4749.26021276178,557.738604134145,3.66933292193517,0.724470188862762,29.0610695098891,68.9148765707505,245,0.102395386596455
medv,rpart1SE,YeoJohnson,0.00300000000004275,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,246,0.0511976932991975
medv,rpart2,YeoJohnson,0.0380000000000109,CART,5.79842739999551,5110.50756757884,574.177731254336,3.77748507404169,0.679563078785055,23.2684210526316,71.4878141194627,247,0.648504115114115
medv,rpart,YeoJohnson,0,failed @ fitting,,,,,,,,248,
medv,Linear,YeoJohnson,0,failed @ fitting,,,,,,,,249,
medv,Additive,YeoJohnson,0,failed @ fitting,,,,,,,,250,
medv,MARS,YeoJohnson,0.00200000000040745,MARS,4.24048615396848,2733.22186894376,399.295569620782,2.62694453697883,0.485715372253715,29.1285530431765,52.2802244538387,251,0.0365327946913391
medv,MARS3,YeoJohnson,0.00199999999949796,MARS,4.02454290381192,2461.93572886268,415.351393807852,2.73257495926219,0.452225855546156,20.2485432459472,49.6178972636153,252,0.0365327946747259
medv,PolyMARS,YeoJohnson,0.00500000000010914,PolyMARS,4.14534670775487,2611.95269777911,398.486405731937,2.62162109034169,0.467550714965535,25.2623861205008,51.107266584891,253,0.0913319867117345
medv,PolyMARS3,YeoJohnson,0.00799999999981083,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,254,0.14613117873213
medv,StepLinear,YeoJohnson,0,failed @ fitting,,,,,,,,255,
medv,PolyMARS_GCV1,YeoJohnson,0.00800000000072032,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,256,0.146131178748743
medv,mda_p01,expoTrans,0.00199999999949796,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,257,0.0365327946747259
medv,polymars_p01,expoTrans,0.00900000000001455,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,258,0.164397576077799
medv,rpart_anova_direct,expoTrans,0.00199999999949796,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,259,0.0365327946747259
medv,bayesglm,expoTrans,0.00299999999970169,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,260,0.0547991920203954
medv,rlm,expoTrans,0,failed @ fitting,,,,,,,,261,
medv,gaussprLinear,expoTrans,0.00100000000020373,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,262,0.0182663973456695
medv,glm,expoTrans,0.00199999999949796,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,263,0.0365327946747259
medv,lmStepAIC,expoTrans,0.00199999999949796,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,264,0.0365327946747259
medv,glmStepAIC,expoTrans,0.00200000000040745,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,265,0.0365327946913391
medv,leapBackward,expoTrans,0.0500000000001819,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,266,0.913319867100732
medv,leapForward,expoTrans,0.0540000000000873,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,267,0.986385456466797
medv,leapSeq,expoTrans,0.055000000000291,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,268,1.00465185381247
medv,treebag,expoTrans,0.0149999999994179,Bagged CART,5.28119344908616,4239.43264549393,480.864063248572,3.16357936347745,0.567736223486784,26.6452247569859,65.1109256998695,269,0.27399596011859
medv,MARS,YeoJohnson,0.000999999999976353,MARS,4.24048615396848,2733.22186894376,399.295569620782,2.62694453697883,0.485715372253715,29.1285530431765,52.2802244538387,251,0.0170658977657525
medv,MARS3,YeoJohnson,0.000999999999976353,MARS,4.02454290381192,2461.93572886268,415.351393807852,2.73257495926219,0.452225855546156,20.2485432459472,49.6178972636153,252,0.0170658977657525
medv,PolyMARS,YeoJohnson,0.00499999999999545,PolyMARS,4.14534670775487,2611.95269777911,398.486405731937,2.62162109034169,0.467550714965535,25.2623861205008,51.107266584891,253,0.0853294888307024
medv,PolyMARS3,YeoJohnson,0.0080000000000382,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,254,0.1365271821299
medv,StepLinear,YeoJohnson,0.000999999999976353,failed @ fitting,,,,,,,,255,
medv,PolyMARS_GCV1,YeoJohnson,0.00699999999994816,PolyMARS,3.93056755668933,2348.30292029021,378.307962222328,2.48886817251532,0.430989948612269,24.7979742486995,48.4592913721426,256,0.119461284362207
medv,mda_p01,expoTrans,0.00100000000009004,mars,4.05745851659644,2502.37138131295,428.103651952539,2.8164713944246,0.482033820325043,19.8139729483085,50.0237081923457,257,0.0170658977676926
medv,polymars_p01,expoTrans,0.0080000000000382,polymars,4.60774851279089,3227.16464628326,470.341645980437,3.09435293408182,0.50063604598065,25.6897871801128,56.8081389088153,258,0.1365271821299
medv,rpart_anova_direct,expoTrans,0.00199999999995271,rpart,24.6174069561974,92114.5422375493,3389.33814190625,22.2982772493832,12.9050933195,49.96789,303.503776315138,259,0.0341317955315049
medv,bayesglm,expoTrans,0.00200000000006639,Bayesian Generalized Linear Model,5.42567950000406,4474.57570158817,543.621796629905,3.57645918835464,0.682562883208476,28.20919367642,66.8922693708934,260,0.0341317955334451
medv,rlm,expoTrans,0.000999999999976353,failed @ fitting,,,,,,,,261,
medv,gaussprLinear,expoTrans,0.00100000000009004,Gaussian Process,5.43233593623491,4485.5616060646,543.603850941929,3.57634112461795,0.684846695684526,28.2853406423929,66.9743354283162,262,0.0170658977676926
medv,glm,expoTrans,0.00200000000006639,Generalized Linear Model,5.42386819205534,4471.58861704802,543.624660037285,3.57647802656108,0.682833794547201,28.1968092191595,66.8699380667279,263,0.0341317955334451
medv,lmStepAIC,expoTrans,0.00200000000006639,Linear Regression with Stepwise Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,264,0.0341317955334451
medv,glmStepAIC,expoTrans,0.00200000000006639,Generalized Linear Model with Stepwise Feature Selection,5.38219690446979,4403.14261480959,540.44508463649,3.55555976734533,0.686520060805561,27.6230156527329,66.3561799292997,265,0.0341317955334451
medv,leapBackward,expoTrans,0.0460000000000491,Linear Regression with Backwards Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,266,0.785031297244015
medv,leapForward,expoTrans,0.0469999999999118,Linear Regression with Forward Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200604,28.1884518732283,66.829855971855,267,0.802097195007827
medv,leapSeq,expoTrans,0.0510000000000446,Linear Regression with Stepwise Selection,5.42061710485925,4466.22964921888,543.926506101538,3.57846385593117,0.682815438200603,28.1884518732282,66.829855971855,268,0.870360786074717
medv,treebag,expoTrans,0.01400000000001,Bagged CART,5.28119344908616,4239.43264549393,480.864063248572,3.16357936347745,0.567736223486784,26.6452247569859,65.1109256998695,269,0.238922568726355
medv,lda,expoTrans,0,failed @ fitting,,,,,,,,270,
medv,ppr,expoTrans,0.00200000000040745,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,271,0.0365327946913391
medv,qrnn,expoTrans,0.00200000000040745,Quantile Regression Neural Network,8.35065100040636,10599.4725638493,624.498646689772,4.10854372822219,0.515996189035309,49.1224111894695,102.953739921624,272,0.0365327946913391
medv,rbf,expoTrans,0.0159999999996217,Radial Basis Function Network,9.64628331434287,14143.7188306466,1003.26051635742,6.60039813393041,89.3877993104639,27.8127899169922,118.927367879082,273,0.29226235746426
medv,ppr,expoTrans,0.00199999999995271,Projection Pursuit Regression,4.69444929078614,3349.7538298519,461.357592334384,3.03524731798937,0.492488589561609,23.9718899465597,57.8770578886998,271,0.0341317955315049
medv,qrnn,expoTrans,0.00199999999995271,Quantile Regression Neural Network,8.35065100040636,10599.4725638493,624.498646689772,4.10854372822219,0.515996189035309,49.1224111894695,102.953739921624,272,0.0341317955315049
medv,rbf,expoTrans,0.0170000000000528,Radial Basis Function Network,9.64628331434287,14143.7188306466,1003.26051635742,6.60039813393041,89.3877993104639,27.8127899169922,118.927367879082,273,0.290120262025552
medv,Boruta,expoTrans,0,failed @ fitting,,,,,,,,274,
medv,qrf,expoTrans,0.0419999999994616,Quantile Random Forest,6.88034495577993,7195.5503,712.49,4.68743421052632,1.1012268814598,35,84.8265895813335,275,0.767188688351989
medv,parRF,expoTrans,0.0100000000002183,Parallel Random Forest,4.24132272966116,2734.30041156533,407.885582010582,2.68345777638541,0.519411209000883,20.4847354497355,52.2905384516677,276,0.182663973423469
medv,qrf,expoTrans,0.0459999999998217,Quantile Random Forest,6.88034495577993,7195.5503,712.49,4.68743421052632,1.1012268814598,35,84.8265895813335,275,0.785031297240134
medv,parRF,expoTrans,0.0100000000002183,Parallel Random Forest,4.24132272966116,2734.30041156533,407.885582010582,2.68345777638541,0.519411209000883,20.4847354497355,52.2905384516677,276,0.170658977665285
medv,Rborist,expoTrans,0,failed @ fitting,,,,,,,,277,
medv,cforest,expoTrans,0.341999999999644,Conditional Inference Random Forest,4.95028326256048,3724.80626569714,454.36480836095,2.98924216026941,0.547685369530421,27.022472299169,61.0311909247816,278,6.24710789093976
medv,cforest,expoTrans,0.329000000000178,Conditional Inference Random Forest,4.95028326256048,3724.80626569714,454.36480836095,2.98924216026941,0.547685369530421,27.022472299169,61.0311909247816,278,5.61468036506837
medv,blackboost,expoTrans,0,failed @ fitting,,,,,,,,279,
medv,kernelpls,expoTrans,0.00199999999949796,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,280,0.0365327946747259
medv,pcr,expoTrans,0.00199999999949796,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,281,0.0365327946747259
medv,pls,expoTrans,0.00199999999949796,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,282,0.0365327946747259
medv,simpls,expoTrans,0.00199999999949796,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,283,0.0365327946747259
medv,kernelpls,expoTrans,0.00300000000015643,Partial Least Squares,5.42386819205533,4471.58861704802,543.624660037282,3.57647802656107,0.682833794547197,28.1968092191596,66.8699380667279,280,0.0511976933011377
medv,pcr,expoTrans,0.00199999999995271,Principal Component Analysis,5.59519078768872,4758.53631249678,551.777973438867,3.63011824630833,0.671270452834862,29.0814530292963,68.9821448818227,281,0.0341317955315049
medv,pls,expoTrans,0.00199999999995271,Partial Least Squares,5.58696230668208,4744.55046807553,551.434161955752,3.62785632865626,0.672275763658373,29.0360421030358,68.8806973547418,282,0.0341317955315049
medv,simpls,expoTrans,0.00200000000018008,Partial Least Squares,5.58696230660709,4744.55046794815,551.434161948198,3.62785632860657,0.672275763651848,29.0360421027633,68.8806973538172,283,0.0341317955353852
medv,enpls,expoTrans,0,failed @ fitting,,,,,,,,284,
medv,icr,expoTrans,0.00700000000051659,Independent Component Regression,5.42386819205533,4471.58861704802,543.624660037283,3.57647802656107,0.682833794547197,28.1968092191595,66.8699380667278,285,0.127864781403074
medv,lars,expoTrans,0.0219999999999345,Least Angle Regression,5.42440516789235,4472.4740566695,543.634320600418,3.57654158289749,0.682978565211512,28.2014531869601,66.8765583494658,286,0.401860741521664