Regression on E2006 tfidf
0.139MSEAPG+
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| APG+CPU=4.7, GE=31, CG=72022.11 | 0.139 | — | — | — | |
| APG-LL+CPU=8.6, GE=75, CG=72022.11 | 0.139 | — | — | — | |
| APG-LL+Sparsity level (s)=m, CPU=69.3, GE=370, CG=82022.11 | 0.148 | — | — | — | |
| APG+Sparsity level (s)=m, CPU=67.8, GE=351, CG=82022.11 | 0.151 | — | — | — | |
| PG-LLSparsity level (s)=m, CPU=906.8, GE=4832, CG=02022.11 | 0.151 | — | — | — | |
| APG+Sparsity level (s)=[1.1m], CPU=69.4, GE=361, CG=82022.11 | 0.151 | — | — | — | |
| PG-LLSparsity level (s)=[1.1m], CPU=909.6, GE=4836, CG=02022.11 | 0.151 | — | — | — | |
| PG-LLSparsity level (s)=1.5m2022.11 | 0.151 | 927.8 | 4,848 | 0 | |
| APG-LL+Sparsity level (s)=1.5m2022.11 | 0.151 | 72.8 | 382 | 8 | |
| PG-LLSparsity level (s)=2m2022.11 | 0.151 | 948.6 | 4,856 | 0 | |
| PG-LLCPU=512.3, GE=4602, CG=02022.11 | 0.151 | — | — | — | |
| PGSparsity level (s)=m, CPU=*1821.0, GE=10000, CG=02022.11 | 0.152 | — | — | — | |
| PGSparsity level (s)=[1.1m], CPU=*1819.9, GE=10000, CG=02022.11 | 0.152 | — | — | — | |
| PGSparsity level (s)=1.5m2022.11 | 0.152 | 1,870.2 | 10,000 | 0 | |
| PGSparsity level (s)=2m2022.11 | 0.152 | 1,894.5 | 10,000 | 0 | |
| PGCPU=1086.3, GE=10000, CG=02022.11 | 0.152 | — | — | — | |
| APG+Sparsity level (s)=1.5m2022.11 | 0.153 | 89.5 | 463 | 8 | |
| APG-LL+Sparsity level (s)=2m2022.11 | 0.153 | 75 | 390 | 8 | |
| APGCPU=4.7, GE=33, CG=02022.11 | 0.153 | — | — | — | |
| APG-LL+Sparsity level (s)=[1.1m], CPU=72.8, GE=384, CG=02022.11 | 0.154 | — | — | — | |
| APGSparsity level (s)=m, CPU=67.4, GE=353, CG=02022.11 | 0.155 | — | — | — | |
| APGSparsity level (s)=[1.1m], CPU=69.0, GE=363, CG=02022.11 | 0.155 | — | — | — | |
| APGSparsity level (s)=1.5m2022.11 | 0.155 | 89 | 465 | 0 | |
| APGSparsity level (s)=2m2022.11 | 0.155 | 97 | 500 | 0 | |
| APG+Sparsity level (s)=2m2022.11 | 0.155 | 97.2 | 498 | 8 |