ServerRun 28471
Creatorwcukierski
Programminimalist-boost
DatasetSyntheticDataProblem
Task typeMulticlassClassification
Created1y286d ago
Done! Flag_green
22s
67M
BinaryClassification
10s
0
0s
0.241
1s

Log file

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iteration:949 feature:7 threshold:-1.17184 min-objective:0.996387
iteration:950 feature:16 threshold:-1.3099 min-objective:0.995822
iteration:951 feature:39 threshold:2.45215 min-objective:0.995882
iteration:952 feature:29 threshold:-4.2651 min-objective:0.995984
iteration:953 feature:36 threshold:3.00469 min-objective:0.995081
iteration:954 feature:16 threshold:-2.21913 min-objective:0.996045
iteration:955 feature:33 threshold:2.48726 min-objective:0.995814
iteration:956 feature:18 threshold:0.499802 min-objective:0.995669
iteration:957 feature:26 threshold:0.981314 min-objective:0.995293
iteration:958 feature:36 threshold:2.19809 min-objective:0.995587
iteration:959 feature:18 threshold:0.164578 min-objective:0.996316
iteration:960 feature:2 threshold:1.34433 min-objective:0.996425
iteration:961 feature:13 threshold:-8.78415 min-objective:0.996511
iteration:962 feature:29 threshold:0.468485 min-objective:0.996155
iteration:963 feature:40 threshold:3.87881 min-objective:0.995924
iteration:964 feature:24 threshold:-11.738 min-objective:0.995729
iteration:965 feature:23 threshold:4.73337 min-objective:0.996205
iteration:966 feature:23 threshold:3.86213 min-objective:0.993808
iteration:967 feature:27 threshold:-0.727294 min-objective:0.996262
iteration:968 feature:12 threshold:-3.01774 min-objective:0.996169
iteration:969 feature:18 threshold:2.51828 min-objective:0.996335
iteration:970 feature:18 threshold:0.292166 min-objective:0.995772
iteration:971 feature:25 threshold:-1.62903 min-objective:0.996331
iteration:972 feature:37 threshold:1.03243 min-objective:0.996423
iteration:973 feature:24 threshold:-6.89978 min-objective:0.995882
iteration:974 feature:30 threshold:-4.00067 min-objective:0.996074
iteration:975 feature:7 threshold:5.97465 min-objective:0.996342
iteration:976 feature:7 threshold:4.66317 min-objective:0.99614
iteration:977 feature:35 threshold:2.1272 min-objective:0.995696
iteration:978 feature:31 threshold:-0.183077 min-objective:0.996158
iteration:979 feature:31 threshold:0.789845 min-objective:0.994178
iteration:980 feature:21 threshold:-3.58626 min-objective:0.996469
iteration:981 feature:40 threshold:2.08808 min-objective:0.996372
iteration:982 feature:15 threshold:0.976609 min-objective:0.996393
iteration:983 feature:8 threshold:-4.96919 min-objective:0.995617
iteration:984 feature:29 threshold:-3.66668 min-objective:0.996416
iteration:985 feature:23 threshold:2.89941 min-objective:0.996019
iteration:986 feature:12 threshold:0.999735 min-objective:0.996018
iteration:987 feature:12 threshold:1.46293 min-objective:0.996069
iteration:988 feature:30 threshold:-0.874257 min-objective:0.996393
iteration:989 feature:5 threshold:5.96031 min-objective:0.996179
iteration:990 feature:35 threshold:-1.2201 min-objective:0.99596
iteration:991 feature:1 threshold:2.19716 min-objective:0.996392
iteration:992 feature:37 threshold:-3.00435 min-objective:0.9963
iteration:993 feature:1 threshold:0.688372 min-objective:0.996214
iteration:994 feature:19 threshold:-1.82537 min-objective:0.99617
iteration:995 feature:19 threshold:-2.06203 min-objective:0.996049
iteration:996 feature:34 threshold:-1.21416 min-objective:0.996167
iteration:997 feature:39 threshold:-0.497544 min-objective:0.995788
iteration:998 feature:26 threshold:-2.24132 min-objective:0.996505
iteration:999 feature:36 threshold:0.58627 min-objective:0.996449
=== END program2: ./run learn ../program1/data --- OK [10s]
=== END program1: ./run learn ../dataset3/train --- OK [10s]

===== 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 [0s]
=== 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 [0s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [0s]

===== 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 [1s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [1s]


real	0m21.700s
user	0m12.785s
sys	0m1.008s

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