Research Article
An Ensemble Multilabel Classification for Disease Risk Prediction
Table 6
Random forest classifier parameter tuning on partition training subsets.
| NumFeatures | NumTrees | Accuracy | Out of bag error | Time out(s) |
| 10 | 30 | 0.9116 | 0.1483 | 2.22 | 10 | 40 | 0.9118 | 0.1473 | 2.94 | 10 | 50 | 0.9126 | 0.1466 | 3.78 | 10 | 60 | 0.9136 | 0.146 | 4.51 | 15 | 30 | 0.9175 | 0.1394 | 2.88 | 15 | 40 | 0.9167 | 0.1387 | 3.88 | 15 | 50 | 0.9185 | 0.138 | 4.99 | 15 | 60 | 0.9185 | 0.1377 | 5.84 | 15 | 70 | 0.9195 | 0.1373 | 6.84 | 15 | 80 | 0.9197 | 0.1373 | 7.83 | 15 | 100 | 0.9185 | 0.1369 | 9.99 | 20 | 30 | 0.9150 | 0.137 | 3.69 | 20 | 50 | 0.9189 | 0.1354 | 6.25 | 20 | 70 | 0.9186 | 0.1346 | 8.76 | 30 | 40 | 0.9173 | 0.1347 | 7.27 | 30 | 60 | 0.9170 | 0.1342 | 10.93 | 40 | 40 | 0.9190 | 0.1343 | 9.09 | 40 | 50 | 0.9195 | 0.1338 | 11.36 | 40 | 60 | 0.9202 | 0.1334 | 13.84 |
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