Leaderboard

Grand Champion and $1,000 prize winner:
NosferatoCorp (Andrzej Janusz)
University of Warsaw, Faculty of Mathematics, Informatics and Mechanics

Runner Up:
UniQ (Vladimir Nikulin)
University of Queensland, Department of Mathematics

Bronze:
barneso (Jeremy Barnes)
Barneso Consulting

Commendations:
LatentView (C. Balakarmekan, R. Boobesh)

ADM1 (Tom Au, Rong Duan, Guangqin Ma, Rensheng Wang)
AT&T Labs, Inc.-Research, USA

Team Reports can be downloaded in a single PDF file here (3.74 MB)

Champions League - Final Standings
	Medium				Large
Team Name	RMSE	Rank	Gini	Rank	RMSE	Rank	Gini	Rank	Points
NosferatoCorp	881.650	2	0.392	1	865.861	1	0.6941	2	9
UniQ	881.884	5	0.3879	2	866.582	3	0.6972	1	15
barneso	882.125	8	0.387	3	866.514	2	0.6923	4	23
LatentView	881.322	1	0.3693	6	866.809	7	0.692	6	33
Kranf	882.383	10	0.3819	4	867.172	9	0.6924	3	38
hugojair	881.722	3	0.3643	10	866.738	5	0.6884	9	41
Baseline1	881.98	6	0.3687	8	866.785	6	0.6903	8	42
Innovative analysts	882.879	12	0.3691	7	868.541	11	0.6918	7	55
axct	881.985	7	0.3674	9	870.801	15	0.6921	5	56
tkstks	881.755	4	0.3613	13	867.273	10	0.6831	10	57
EnsembleMaster09	882.656	11	0.2703	19	866.643	4	0.522	19	76
Edr2	882.211	9	0.3353	16	869.127	13	0.6671	15	81
DMLab	883.038	13	0.3153	18	866.985	8	0.635	18	83
The final say	889.101	19	0.3694	5	873.55	18	0.6759	12	84
Team_EXL	886.394	18	0.3421	15	868.555	12	0.6686	14	85
Baseline2	885.299	15	0.3635	12	873.219	17	0.6702	13	87
DreamTeam	885.412	16	0.3635	11	873.638	19	0.6778	11	87
TULIP	884.128	14	0.3217	17	869.216	14	0.662	17	93
albert2	894.071	20	0.3606	14	883.029	20	0.6638	16	106
Green Ensemble	886.367	17	0.1327	20	872.25	16	0.3572	20	109
TUB09	7858.22	21	0.0315	21	8120.22	21	0.1234	21	126

medium RMSE
Rank	Team Name	RMSE	Method
1	LatentView	881.322
	The Champions	881.627	Average - Nosferato,UniQ,barneso
2	NosferatoCorp	881.650	crippledGMB
3	hugojair	881.722	bipso_blr_so12-Nov-2009091014_MEDIUM_RM
4	tkstks	881.755	Ensemble Selction
5	UniQ	881.884	lm_m
6	Baseline	881.98	Linear Regression Ensemble
7	axct	881.985	s150
8	barneso	882.125	Merged model
9	Edr2	882.211	Single Linear Perceptron
10	Kranf	882.383	elasticnet
	The Ensemble	882.457	Average All Entries
11	EnsembleMaster09	882.656	LASSO
12	Innovative analysts	882.879
13	DMLab	883.038
14	ISMLL	883.109
15	BusinessResearch	883.852	MLP ensemble after stepwise selection
16	TULIP	884.128
17	Combador	884.643	Please refer to the report
18	Baseline	885.299	Linear Regression
19	DreamTeam	885.412	M_RMSE_ScoreMY1column.txt
20	Green Ensemble	886.367
21	Team_EXL	886.394	IE with random sets
22	Baseline	888.649	Average Top 10 Experts
23	The final say	889.101
24	KKUI TU Kosice	889.844	avg some models
25	Baseline	892.249	Best Expert
26	albert2	894.071
27	Baseline	894.827	Average All Experts
28	Baseline	947.88	Worst Expert
29	snustat	1461.93	random forest
30	snustat_mk	1461.93	rf
31	TUB09	7858.22	ComSUM

medium AUC
Rank	Team Name	Gini	Method
	The Champions	0.3928	Average Rank - Nosferato,UniQ,barneso
1	NosferatoCorp	0.392008	GeneticMetaBlender
2	UniQ	0.387888	glm_m
3	barneso	0.386991	Merged model
4	Kranf	0.381865	elasticnet
5	ADM1	0.381485	M_ALL5_Score
	The Ensemble	0.3769	Average Rank All Entries
6	The final say	0.369404
7	LatentView	0.369343
8	Innovative analysts	0.369093
9	Baseline	0.3687	Logistic Regression Ensemble
10	axct	0.367385	s50
11	hugojair	0.36432	bipso_blr_so19-Nov-2009135305_auc_MEDIU
12	DreamTeam	0.363502	LogReg1version
13	Baseline	0.3635	Logistic Regression
14	tkstks	0.361324
15	albert2	0.360562
16	NeuroTech RDI	0.360495	Zoomed MLP
17	Team_EXL	0.342139	NN+BLASTING+IE
18	Edr2	0.335274	Best Combo of 10 Experts by GINI
19	Baseline	0.3284	Average Top 10 Experts
20	Baseline	0.3243	Best Expert
21	Baseline	0.3218	Average All Experts
22	TULIP	0.321704
23	DMLab	0.315261
24	EnsembleMaster09	0.270304	Gradient Boosting
25	Baseline	0.2522	Worst Expert
26	Green Ensemble	0.13271
27	TUB09	0.0315204	Comb

large RMSE
Rank	Team Name	RMSE	Method
1	NosferatoCorp	865.861	crippledGMB
	The Champions	865.969	Average - Nosferato,UniQ,barneso
	The Ensemble	866.160	Average All Entries
2	barneso	866.514	Merged model
3	UniQ	866.582	lm_L
4	EnsembleMaster09	866.643	LASSO
5	hugojair	866.738	bipso_blr_so19-Nov-2009131111_LARGE_RMS
6	Baseline	866.785	Linear Regression Ensemble
7	LatentView	866.809
8	DMLab	866.985	General linear
9	Kranf	867.172	elasticnet
10	tkstks	867.273	RSS003
11	ISMLL	867.875
12	BusinessResearch	868.312	MLP ensemble after stepwise selection
13	Innovative analysts	868.541
14	Team_EXL	868.555	Coefficient Blasting and bootstrapping
15	Edr2	869.127	Single Linear Perceptron
16	TULIP	869.216
17	axct	870.801	s300
18	Green Ensemble	872.25
19	Baseline	873.204	Average Top 10 Experts
20	Baseline	873.219	Linear Regression
21	The final say	873.55
22	DreamTeam	873.638	Reg1version
23	Baseline	880.269	Best Expert
24	Baseline	880.766	Average All Experts
25	albert2	883.029
26	Baseline	933.278	Worst Expert
27	TUB09	8120.22

large AUC
Rank	Team Name	Gini	Method
1	ADM1	0.698372	L_ALL7_Score
	The Champions	0.697159	Average Rank - Nosferato,UniQ,barneso
2	UniQ	0.697161	glm_L
3	NosferatoCorp	0.69407	GeneticMetaBlender
	The Ensemble	0.69240	Ave Rank All Entries
4	Kranf	0.692358	elasticnet
5	barneso	0.692293	Merged model
6	axct	0.692122	s6
7	LatentView	0.691992
8	Innovative analysts	0.691773
9	Baseline	0.6903	Logistic Regression Ensemble
10	hugojair	0.688352	bipso_blr_so19-Nov-2009171830_auc_LARGe
11	tkstks	0.683087	es006
12	NeuroTech RDI	0.681153	MLP
13	DreamTeam	0.677822	L_AUC_ScoreMY1column.txt
14	The final say	0.675946
15	Baseline	0.6702	Logistic Regression
16	Team_EXL	0.668552	Blasting + PCA + NN
17	Edr2	0.667128	Best Combo of 10 Experts by GINI
18	albert2	0.663776
19	TULIP	0.661976
20	Baseline	0.6592	Average Top 10 Experts
21	Baseline	0.6532	Average All Experts
22	Baseline	0.6526	Best Expert
23	DMLab	0.635049
24	Baseline	0.5564	Worst Expert
25	EnsembleMaster09	0.521953	LASSO
26	Green Ensemble	0.357228	regression tree
27	TUB09	0.123444	Comb

AusDM 2009