 | Run 16039 |
| Creator | emrah |
| Program | minimalist-boost |
| Dataset | s21 |
| Task type | MulticlassClassification |
| Created | 1y52d ago |
Done! 
| 1m22s | |
| 80M | |
| BinaryClassification | |
| 0.007 | |
| 0.018 | |
Log file
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(lines omitted) ... iteration:627 feature:3 threshold:5.8283 min-objective:0.999393 iteration:628 feature:3 threshold:5.36043 min-objective:0.999398 iteration:629 feature:10 threshold:-2.59655 min-objective:0.999358 iteration:630 feature:12 threshold:-5.00063 min-objective:0.999243 iteration:631 feature:8 threshold:-5.84429 min-objective:0.99918 iteration:632 feature:2 threshold:0.825 min-objective:0.999211 iteration:633 feature:8 threshold:-5.97762 min-objective:0.999397 iteration:634 feature:12 threshold:0.519131 min-objective:0.999337 iteration:635 feature:10 threshold:4.98782 min-objective:0.999378 iteration:636 feature:10 threshold:5.91183 min-objective:0.9993 iteration:637 feature:3 threshold:4.24125 min-objective:0.999414 iteration:638 feature:5 threshold:3.86944 min-objective:0.999387 iteration:639 feature:10 threshold:0.0606677 min-objective:0.999399 iteration:640 feature:10 threshold:-5.37676 min-objective:0.999455 iteration:641 feature:1 threshold:0.825 min-objective:0.999489 iteration:642 feature:12 threshold:-1.73271 min-objective:0.999488 iteration:643 feature:2 threshold:0.925 min-objective:0.99933 iteration:644 feature:5 threshold:0.0726554 min-objective:0.999278 iteration:645 feature:10 threshold:5.91784 min-objective:0.999459 iteration:646 feature:7 threshold:2.95767 min-objective:0.999448 iteration:647 feature:10 threshold:3.28832 min-objective:0.999445 iteration:648 feature:12 threshold:2.04764 min-objective:0.999435 iteration:649 feature:5 threshold:2.04753 min-objective:0.99925 iteration:650 feature:6 threshold:-2.63571 min-objective:0.999448 iteration:651 feature:14 threshold:-0.0899674 min-objective:0.999431 iteration:652 feature:14 threshold:-0.580363 min-objective:0.999457 iteration:653 feature:13 threshold:-5.76155 min-objective:0.99944 iteration:654 feature:6 threshold:-1.92161 min-objective:0.999471 iteration:655 feature:6 threshold:-1.03594 min-objective:0.999349 iteration:656 feature:6 threshold:-0.525799 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min-objective:0.999553 iteration:957 feature:15 threshold:-5.42492 min-objective:0.999694 iteration:958 feature:15 threshold:-3.47555 min-objective:0.999579 iteration:959 feature:15 threshold:-2.07163 min-objective:0.9995 iteration:960 feature:4 threshold:-4.3897 min-objective:0.999607 iteration:961 feature:15 threshold:-5.61143 min-objective:0.999679 iteration:962 feature:15 threshold:-5.88666 min-objective:0.999622 iteration:963 feature:11 threshold:-5.65327 min-objective:0.999692 iteration:964 feature:9 threshold:-4.01498 min-objective:0.999681 iteration:965 feature:9 threshold:-5.7857 min-objective:0.999556 iteration:966 feature:9 threshold:-5.44634 min-objective:0.999668 iteration:967 feature:12 threshold:3.19735 min-objective:0.999635 iteration:968 feature:5 threshold:-1.26423 min-objective:0.999641 iteration:969 feature:10 threshold:4.98782 min-objective:0.99956 iteration:970 feature:8 threshold:4.44169 min-objective:0.999518 iteration:971 feature:7 threshold:2.95767 min-objective:0.999568 iteration:972 feature:7 threshold:5.11599 min-objective:0.999412 iteration:973 feature:7 threshold:5.30973 min-objective:0.99953 iteration:974 feature:7 threshold:5.23118 min-objective:0.999614 iteration:975 feature:7 threshold:4.97497 min-objective:0.999633 iteration:976 feature:2 threshold:0.925 min-objective:0.999688 iteration:977 feature:10 threshold:0.488502 min-objective:0.999618 iteration:978 feature:12 threshold:-1.69115 min-objective:0.999594 iteration:979 feature:5 threshold:1.88407 min-objective:0.999485 iteration:980 feature:10 threshold:-5.24021 min-objective:0.999693 iteration:981 feature:2 threshold:0.825 min-objective:0.999705 iteration:982 feature:12 threshold:2.04764 min-objective:0.999565 iteration:983 feature:4 threshold:-5.60746 min-objective:0.999666 iteration:984 feature:4 threshold:-5.20569 min-objective:0.999525 iteration:985 feature:4 threshold:-4.98104 min-objective:0.999536 iteration:986 feature:6 threshold:-2.42793 min-objective:0.999572 iteration:987 feature:6 threshold:-0.218004 min-objective:0.99957 iteration:988 feature:6 threshold:-0.0978757 min-objective:0.999426 iteration:989 feature:6 threshold:-0.218004 min-objective:0.999516 iteration:990 feature:6 threshold:-0.0978757 min-objective:0.999591 iteration:991 feature:6 threshold:0.27052 min-objective:0.99958 iteration:992 feature:5 threshold:-4.03301 min-objective:0.999601 iteration:993 feature:5 threshold:-4.96615 min-objective:0.999543 iteration:994 feature:6 threshold:0.827357 min-objective:0.999639 iteration:995 feature:10 threshold:3.1219 min-objective:0.999577 iteration:996 feature:5 threshold:-4.25338 min-objective:0.999613 iteration:997 feature:15 threshold:-5.93229 min-objective:0.999636 iteration:998 feature:15 threshold:-5.42492 min-objective:0.999691 iteration:999 feature:15 threshold:-5.61143 min-objective:0.999687 === END program2: ./run learn ../program1/data --- OK [70s] === END program1: ./run learn ../dataset3/train --- OK [72s] ===== MAIN: predict/evaluate on train data ===== === START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in === END program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in --- OK [1s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out === START program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output === END program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output --- OK [1s] === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s] === START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out === END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [1s] ===== MAIN: predict/evaluate on test data ===== === START program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in === END program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in --- OK [1s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out === START program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output === END program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output --- OK [0s] === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out === END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [1s] real 1m27.147s user 0m44.587s sys 0m3.272s
Run specification 
Results
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