| target,target_name,model,family,is_baseline,task,notes,fit_seconds,mae,rmse,median_ae,r2,poisson_deviance,precision_at_10,ndcg_at_10,precision_at_50,ndcg_at_50,kendall_tau,status,peak_rss_gb,n_features,baseline_model,baseline_metric,beats_baseline,improvement_pct,pr_auc,roc_auc,brier,f1_best,threshold_best,prevalence |
| A1,parking_intensity,mean,baseline,True,regression,training mean; absolute floor,0.01,0.21914739880112935,1.1188749580216464,0.12099105630842004,-0.00021001473471704557,0.8775469874345732,0.0,0.011836678227344,0.02,0.04359245856709398,,ok,2.45,63,mean,0.8775469874345732,False,0.0,,,,,, |
| A1,parking_intensity,seasonal_naive,baseline,True,regression,"same cell, same hour, one week earlier",0.0,0.18092604212984703,1.4625021548471049,0.0,-0.7089175909565442,3.5433803733331763,0.9,0.9892596488518955,0.94,0.9894062538383603,0.7874110090786781,ok,3.0,63,mean,0.8775469874345732,False,-303.78,,,,,, |
| A1,parking_intensity,cell_hour_mean,baseline,True,regression,per-cell hour-of-week historical mean,0.07,0.18129081343487274,1.0917370605397225,0.0,0.04772102118075083,1.3909762113409176,0.9,0.9640526701091585,0.74,0.9368908812103371,0.4254233878612323,ok,3.0,63,mean,0.8775469874345732,False,-58.51,,,,,, |
| A1,parking_intensity,ridge,linear,False,regression,L2 linear; interpretable reference,5.49,0.1777027920570832,1.0533636173798835,0.03127635512551595,0.11348779532827868,0.6026330223644141,1.0,0.9996289938547847,0.94,0.9945573714495903,0.7722372953553022,ok,5.2,63,mean,0.8775469874345732,True,31.33,,,,,, |
| A1,parking_intensity,poisson_glm,linear,False,regression,principled for counts; log link matches the data-generating process,7.46,0.17238256963839677,1.0912341432742674,0.020773541828053473,0.048598168707784994,0.5667641011546074,0.9,0.9909580122948712,0.8,0.9710613164702532,0.6456699628518932,ok,5.77,63,mean,0.8775469874345732,True,35.41,,,,,, |
| A1,parking_intensity,random_forest,trees,False,regression,best performer in the NYC parking-ticket literature,696.26,0.23050329374950873,1.0380061741009423,0.10842567737608316,0.13914904732826983,0.5793188034716594,1.0,0.9976864207756954,0.9,0.9896388578463547,0.40640317094070383,ok,6.23,63,mean,0.8775469874345732,True,33.98,,,,,, |
| A1,parking_intensity,extra_trees,trees,False,regression,variance-reduced contrast to random forest,191.44,0.17843840481242818,1.0341507733972644,0.0298676247036632,0.14553197994277778,0.5085729470163757,1.0,0.996537693098164,0.92,0.9896948756332574,0.7274885748756401,ok,6.23,63,mean,0.8775469874345732,True,42.05,,,,,, |
| A1,parking_intensity,hist_gbm,gbdt,False,regression,sklearn-native GBDT; no extra dependency,5.74,0.17126490771276512,1.0363363937957386,0.03216983943370197,0.14191642163802287,0.5189015076026916,0.9,0.9915726336454136,0.9,0.9869235147865,0.7804673401396617,ok,6.23,63,mean,0.8775469874345732,True,40.87,,,,,, |
| A1,parking_intensity,hist_gbm_poisson,gbdt,False,regression,GBDT with a Poisson objective; regularised to keep the log link stable,18.17,0.16409045802456768,1.0318576855796617,0.020413952731622116,0.14931711058334418,0.5041641594766796,0.9,0.9901673253529395,0.9,0.9860215287207704,0.7540472071701869,ok,6.23,63,mean,0.8775469874345732,True,42.55,,,,,, |
| A1,parking_intensity,xgboost,gbdt,False,regression,GBDT benchmark with a count objective,21.06,0.1655901280028289,1.0283607290168781,0.022760430350899696,0.15507325383289483,0.4999245389994539,0.9,0.9902052182525148,0.9,0.9862101550938679,0.7438854036087347,ok,6.23,63,mean,0.8775469874345732,True,43.03,,,,,, |
| A1,parking_intensity,lightgbm,gbdt,False,regression,fast GBDT; also supplies quantile regression for module B,14.87,0.1671591534524625,1.0372624874862837,0.02326316690533358,0.1403821305365608,0.5054702095359982,0.9,0.981691040657606,0.9,0.9796544111883096,0.6879816897172703,ok,6.23,63,mean,0.8775469874345732,True,42.4,,,,,, |
| A1,parking_intensity,catboost,gbdt,False,regression,ordered target statistics for high-cardinality categoricals,32.52,0.1613680746985717,1.034722383293808,0.024543008806415557,0.14458713249336175,0.5162038659511996,0.9,0.9892766101467407,0.88,0.9839587508600609,0.7338523468797351,ok,6.23,63,mean,0.8775469874345732,True,41.18,,,,,, |
| A1,parking_intensity,torch_mlp,deep,False,regression,"feedforward net, Poisson NLL, on mps",25.7,0.12867566121352803,1.0515041323235892,0.0049816027749329805,0.11661492289116804,0.5787123222049488,1.0,0.9704112990606957,0.86,0.9610687458850966,0.727295454626961,ok,6.23,63,mean,0.8775469874345732,True,34.05,,,,,, |
| A1,parking_intensity,torch_lstm,deep,False,regression,"LSTM over each cell's 48h history, on mps; the only family that sees temporal order rather than unordered lag columns",153.69,0.18833964162423453,1.072359984325349,0.03834306448698044,0.08122474426046666,0.6279387456027464,0.8,0.9704000699425177,0.88,0.9816571506960173,0.880961760999193,ok,6.23,63,mean,0.8775469874345732,True,28.44,,,,,, |
| A1,parking_intensity,graphsage,gnn,False,regression,"two-layer GraphSAGE over the H3 adjacency graph, on mps; learns neighbour aggregation rather than reading precomputed k-ring columns",37.75,0.12275424876166457,1.0660318728161917,0.00011783007357735187,0.09203633272748735,0.776680995688906,0.7,0.9467712094915538,0.74,0.9400960271640959,0.6259533050819804,ok,6.23,63,mean,0.8775469874345732,True,11.49,,,,,, |
| A1,parking_intensity,bayesian_hierarchical,bayesian,False,regression,"partially-pooled Poisson with station-level intercepts, fitted by ADVI; the only family returning posterior intervals",27.28,0.18145894178707808,1.0795479097077174,0.032559525623980186,0.06886654111942869,0.6207159123763161,0.8,0.9521691009084714,0.72,0.9312963237610142,0.5263676301797485,ok,6.23,63,mean,0.8775469874345732,True,29.27,,,,,, |
| A1,parking_intensity,stacked,ensemble,False,regression,"ridge meta-learner over out-of-fold predictions from poisson_glm, hist_gbm, xgboost, lightgbm, catboost",287.54,0.17125071272852946,1.0281228179137705,0.02317931535897212,0.15546415596650487,0.5070625196641482,0.9,0.9911859168407264,0.88,0.9827205624071845,0.7502123908035574,ok,6.23,63,mean,0.8775469874345732,True,42.22,,,,,, |
| A2,hotspot,prior,baseline,True,classification,training prevalence for every row,0.02,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,False,0.0,0.0029089458395841036,0.5,0.0029007703566372683,0.005801016835379573,0.003444186860804607,0.0029089458395841036 |
| A2,hotspot,logistic,linear,False,classification,interpretable reference,10.88,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,True,3774.04,0.11269376297069328,0.9204112493219946,0.0027452898720966307,0.2073772872494917,0.07299212597488189,0.0029089458395841036 |
| A2,hotspot,random_forest,trees,False,classification,,63.87,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,True,4594.05,0.13654750145295197,0.9379834170755537,0.0026770978177126964,0.2311654861558274,0.11357930620071895,0.0029089458395841036 |
| A2,hotspot,extra_trees,trees,False,classification,,22.92,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,True,4087.26,0.12180513138977633,0.9362090465413838,0.00271352774313975,0.21380384016606122,0.07821268922363081,0.0029089458395841036 |
| A2,hotspot,hist_gbm,gbdt,False,classification,,4.04,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,True,2930.24,0.08814802949421606,0.9128124845569581,0.0030555183854272367,0.1895902547065338,0.05887057884372568,0.0029089458395841036 |
| A2,hotspot,xgboost,gbdt,False,classification,,19.92,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,True,4305.18,0.12814438232434555,0.9368098433874605,0.0026957948446798,0.21529443112176927,0.09574083983898163,0.0029089458395841036 |
| A2,hotspot,lightgbm,gbdt,False,classification,,13.94,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,True,1126.62,0.035681704325356015,0.9229296187272968,0.005441218519902834,0.09261417823460845,0.045175080071767716,0.0029089458395841036 |
| A2,hotspot,catboost,gbdt,False,classification,,35.09,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,True,4385.83,0.13049023702366846,0.934104232122837,0.002687162418537367,0.22029988465974623,0.08645075647259819,0.0029089458395841036 |
| A2,hotspot,stacked,ensemble,False,classification,"logistic meta-learner over out-of-fold predictions from logistic, hist_gbm, xgboost, lightgbm, catboost",233.25,,,,,,,,,,,ok,6.23,63,prior,0.0029089458395841036,True,4026.89,0.12004907319817192,0.9363077644191123,0.0028221607664345952,0.1901669758812616,0.07228705399135302,0.0029089458395841036 |
| A3,capture_rejection,prior,baseline,True,classification,training prevalence for every row,0.0,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,False,0.0,0.2459016393442623,0.5,0.1880989065447491,0.3947368421052631,0.2975241483174076,0.2459016393442623 |
| A3,capture_rejection,logistic,linear,False,classification,interpretable reference,0.28,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,True,17.67,0.28934353491863396,0.5642003700819774,0.7210785599827645,0.4068151532457644,0.965719296137278,0.2459016393442623 |
| A3,capture_rejection,random_forest,trees,False,classification,,2.0,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,True,18.72,0.2919371223029594,0.56828217040538,0.5618644396428021,0.4039561718219723,0.778454848961047,0.2459016393442623 |
| A3,capture_rejection,extra_trees,trees,False,classification,,1.07,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,True,14.87,0.2824691009004455,0.5544284084529997,0.457256567314785,0.4027477180765973,0.6802202257324685,0.2459016393442623 |
| A3,capture_rejection,hist_gbm,gbdt,False,classification,,4.3,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,True,20.19,0.29554389576175,0.5574531478765222,0.7166039444839376,0.3960306720793866,0.7667793311770811,0.2459016393442623 |
| A3,capture_rejection,xgboost,gbdt,False,classification,,1.59,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,True,25.57,0.30877406545463415,0.574990291887237,0.712176038585389,0.3962299020513768,0.8728531002998352,0.2459016393442623 |
| A3,capture_rejection,lightgbm,gbdt,False,classification,,2.13,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,True,15.43,0.2838436986298878,0.5522256561108128,0.7249781623305395,0.39535096549830695,0.903491176858297,0.2459016393442623 |
| A3,capture_rejection,catboost,gbdt,False,classification,,3.62,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,True,21.46,0.2986611304240019,0.5630499168019767,0.6816912681208498,0.4033317001469867,0.908946155030489,0.2459016393442623 |
| A3,capture_rejection,stacked,ensemble,False,classification,"logistic meta-learner over out-of-fold predictions from logistic, hist_gbm, xgboost, lightgbm, catboost",48.21,,,,,,,,,,,ok,6.23,35,prior,0.2459016393442623,True,25.41,0.3083810006499804,0.5796144386967051,0.4400876279758309,0.4023306906103735,0.7317729739602562,0.2459016393442623 |
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