nonet
Weighted Average Ensemble without Training Labels
It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.4.0 |
rolling linux/jammy R-4.5 | nonet_0.4.0.tar.gz |
84.9 KiB |
0.4.0 |
rolling linux/noble R-4.5 | nonet_0.4.0.tar.gz |
84.9 KiB |
0.4.0 |
rolling source/ R- | nonet_0.4.0.tar.gz |
67.6 KiB |
0.4.0 |
latest linux/jammy R-4.5 | nonet_0.4.0.tar.gz |
84.9 KiB |
0.4.0 |
latest linux/noble R-4.5 | nonet_0.4.0.tar.gz |
84.9 KiB |
0.4.0 |
latest source/ R- | nonet_0.4.0.tar.gz |
67.6 KiB |
0.4.0 |
2026-04-26 source/ R- | nonet_0.4.0.tar.gz |
67.6 KiB |
0.4.0 |
2026-04-23 source/ R- | nonet_0.4.0.tar.gz |
67.6 KiB |
0.4.0 |
2026-04-09 windows/windows R-4.5 | nonet_0.4.0.zip |
92.2 KiB |
0.4.0 |
2025-04-20 source/ R- | nonet_0.4.0.tar.gz |
67.6 KiB |