 | Run 7979 |
| Creator | internal |
| Program | boostexter |
| Dataset | arcene2 |
| Task type | MulticlassClassification |
| Created | 1y122d ago |
| Download | Login required! |
Done! 
| 1s | |
| 0B | |
| MulticlassClassification | |
| 0.057 | |
| 0.300 | |
Log file
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train= 0.0571429 rnd 861: wh-err= 0.999998 th-err= 0.217334 test= nan train= 0.0571429 rnd 862: wh-err= 0.999998 th-err= 0.217333 test= nan train= 0.0571429 rnd 863: wh-err= 0.999998 th-err= 0.217333 test= nan train= 0.0571429 rnd 864: wh-err= 0.999998 th-err= 0.217332 test= nan train= 0.0571429 rnd 865: wh-err= 0.999998 th-err= 0.217332 test= nan train= 0.0571429 rnd 866: wh-err= 0.999985 th-err= 0.217329 test= nan train= 0.0571429 rnd 867: wh-err= 0.999998 th-err= 0.217328 test= nan train= 0.0571429 rnd 868: wh-err= 0.999964 th-err= 0.217321 test= nan train= 0.0571429 rnd 869: wh-err= 0.999932 th-err= 0.217306 test= nan train= 0.0571429 rnd 870: wh-err= 0.999907 th-err= 0.217286 test= nan train= 0.0571429 rnd 871: wh-err= 0.999853 th-err= 0.217254 test= nan train= 0.0571429 rnd 872: wh-err= 0.999805 th-err= 0.217212 test= nan train= 0.0571429 rnd 873: wh-err= 0.999839 th-err= 0.217177 test= nan train= 0.0571429 rnd 874: wh-err= 0.999949 th-err= 0.217166 test= nan train= 0.0571429 rnd 875: wh-err= 0.999903 th-err= 0.217145 test= nan train= 0.0571429 rnd 876: wh-err= 0.999930 th-err= 0.217129 test= nan train= 0.0571429 rnd 877: wh-err= 0.999960 th-err= 0.217121 test= nan train= 0.0571429 rnd 878: wh-err= 0.999963 th-err= 0.217113 test= nan train= 0.0571429 rnd 879: wh-err= 0.999984 th-err= 0.217109 test= nan train= 0.0571429 rnd 880: wh-err= 0.999998 th-err= 0.217109 test= nan train= 0.0571429 rnd 881: wh-err= 0.999998 th-err= 0.217108 test= nan train= 0.0571429 rnd 882: wh-err= 0.999998 th-err= 0.217108 test= nan train= 0.0571429 rnd 883: wh-err= 0.999998 th-err= 0.217108 test= nan train= 0.0571429 rnd 884: wh-err= 0.999998 th-err= 0.217107 test= nan train= 0.0571429 rnd 885: wh-err= 0.999998 th-err= 0.217107 test= nan train= 0.0571429 rnd 886: wh-err= 0.999998 th-err= 0.217106 test= nan train= 0.0571429 rnd 887: wh-err= 0.999998 th-err= 0.217106 test= nan train= 0.0571429 rnd 888: wh-err= 0.999986 th-err= 0.217103 test= nan train= 0.0571429 rnd 889: wh-err= 0.999998 th-err= 0.217103 test= nan train= 0.0571429 rnd 890: wh-err= 0.999998 th-err= 0.217102 test= nan train= 0.0571429 rnd 891: wh-err= 0.999998 th-err= 0.217102 test= nan train= 0.0571429 rnd 892: wh-err= 0.999998 th-err= 0.217102 test= nan train= 0.0571429 rnd 893: wh-err= 0.999998 th-err= 0.217101 test= nan train= 0.0571429 rnd 894: wh-err= 0.999998 th-err= 0.217101 test= nan train= 0.0571429 rnd 895: wh-err= 0.999998 th-err= 0.217100 test= nan train= 0.0571429 rnd 896: wh-err= 0.999998 th-err= 0.217100 test= nan train= 0.0571429 rnd 897: wh-err= 0.999987 th-err= 0.217097 test= nan train= 0.0571429 rnd 898: wh-err= 0.999966 th-err= 0.217090 test= nan train= 0.0571429 rnd 899: wh-err= 0.999942 th-err= 0.217077 test= nan train= 0.0571429 rnd 900: wh-err= 0.999917 th-err= 0.217059 test= nan train= 0.0571429 rnd 901: wh-err= 0.999877 th-err= 0.217032 test= nan train= 0.0571429 rnd 902: wh-err= 0.999834 th-err= 0.216996 test= nan train= 0.0571429 rnd 903: wh-err= 0.999864 th-err= 0.216967 test= nan train= 0.0571429 rnd 904: wh-err= 0.999953 th-err= 0.216956 test= nan train= 0.0571429 rnd 905: wh-err= 0.999915 th-err= 0.216938 test= nan train= 0.0571429 rnd 906: wh-err= 0.999939 th-err= 0.216925 test= nan train= 0.0571429 rnd 907: wh-err= 0.999964 th-err= 0.216917 test= nan train= 0.0571429 rnd 908: wh-err= 0.999965 th-err= 0.216909 test= nan train= 0.0571429 rnd 909: wh-err= 0.999985 th-err= 0.216906 test= nan train= 0.0571429 rnd 910: wh-err= 0.999986 th-err= 0.216903 test= nan train= 0.0571429 rnd 911: wh-err= 0.999998 th-err= 0.216903 test= nan train= 0.0571429 rnd 912: wh-err= 0.999998 th-err= 0.216902 test= nan train= 0.0571429 rnd 913: wh-err= 0.999998 th-err= 0.216902 test= nan train= 0.0571429 rnd 914: wh-err= 0.999987 th-err= 0.216899 test= nan train= 0.0571429 rnd 915: wh-err= 0.999998 th-err= 0.216899 test= nan train= 0.0571429 rnd 916: wh-err= 0.999998 th-err= 0.216898 test= nan train= 0.0571429 rnd 917: wh-err= 0.999998 th-err= 0.216898 test= nan train= 0.0571429 rnd 918: wh-err= 0.999998 th-err= 0.216898 test= nan train= 0.0571429 rnd 919: wh-err= 0.999998 th-err= 0.216897 test= nan train= 0.0571429 rnd 920: wh-err= 0.999998 th-err= 0.216897 test= nan train= 0.0571429 rnd 921: wh-err= 0.999987 th-err= 0.216894 test= nan train= 0.0571429 rnd 922: wh-err= 0.999998 th-err= 0.216894 test= nan train= 0.0571429 rnd 923: wh-err= 0.999998 th-err= 0.216893 test= nan train= 0.0571429 rnd 924: wh-err= 0.999998 th-err= 0.216893 test= nan train= 0.0571429 rnd 925: wh-err= 0.999999 th-err= 0.216893 test= nan train= 0.0571429 rnd 926: wh-err= 0.999999 th-err= 0.216892 test= nan train= 0.0571429 rnd 927: wh-err= 0.999999 th-err= 0.216892 test= nan train= 0.0571429 rnd 928: wh-err= 0.999987 th-err= 0.216889 test= nan train= 0.0571429 rnd 929: wh-err= 0.999967 th-err= 0.216882 test= nan train= 0.0571429 rnd 930: wh-err= 0.999986 th-err= 0.216879 test= nan train= 0.0571429 rnd 931: wh-err= 0.999998 th-err= 0.216879 test= nan train= 0.0571429 rnd 932: wh-err= 0.999999 th-err= 0.216879 test= nan train= 0.0571429 rnd 933: wh-err= 0.999999 th-err= 0.216878 test= nan train= 0.0571429 rnd 934: wh-err= 0.999999 th-err= 0.216878 test= nan train= 0.0571429 rnd 935: wh-err= 0.999988 th-err= 0.216875 test= nan train= 0.0571429 rnd 936: wh-err= 0.999968 th-err= 0.216868 test= nan train= 0.0571429 rnd 937: wh-err= 0.999987 th-err= 0.216865 test= nan train= 0.0571429 rnd 938: wh-err= 0.999998 th-err= 0.216865 test= nan train= 0.0571429 rnd 939: wh-err= 0.999988 th-err= 0.216862 test= nan train= 0.0571429 rnd 940: wh-err= 0.999998 th-err= 0.216862 test= nan train= 0.0571429 rnd 941: wh-err= 0.999999 th-err= 0.216862 test= nan train= 0.0571429 rnd 942: wh-err= 0.999999 th-err= 0.216861 test= nan train= 0.0571429 rnd 943: wh-err= 0.999999 th-err= 0.216861 test= nan train= 0.0571429 rnd 944: wh-err= 0.999999 th-err= 0.216861 test= nan train= 0.0571429 rnd 945: wh-err= 0.999999 th-err= 0.216861 test= nan train= 0.0571429 rnd 946: wh-err= 0.999999 th-err= 0.216860 test= nan train= 0.0571429 rnd 947: wh-err= 0.999988 th-err= 0.216858 test= nan train= 0.0571429 rnd 948: wh-err= 0.999998 th-err= 0.216857 test= nan train= 0.0571429 rnd 949: wh-err= 0.999999 th-err= 0.216857 test= nan train= 0.0571429 rnd 950: wh-err= 0.999999 th-err= 0.216857 test= nan train= 0.0571429 rnd 951: wh-err= 0.999986 th-err= 0.216854 test= nan train= 0.0571429 rnd 952: wh-err= 0.999998 th-err= 0.216853 test= nan train= 0.0571429 rnd 953: wh-err= 0.999999 th-err= 0.216853 test= nan train= 0.0571429 rnd 954: wh-err= 0.999999 th-err= 0.216853 test= nan train= 0.0571429 rnd 955: wh-err= 0.999999 th-err= 0.216852 test= nan train= 0.0571429 rnd 956: wh-err= 0.999988 th-err= 0.216850 test= nan train= 0.0571429 rnd 957: wh-err= 0.999970 th-err= 0.216843 test= nan train= 0.0571429 rnd 958: wh-err= 0.999987 th-err= 0.216841 test= nan train= 0.0571429 rnd 959: wh-err= 0.999998 th-err= 0.216840 test= nan train= 0.0571429 rnd 960: wh-err= 0.999999 th-err= 0.216840 test= nan train= 0.0571429 rnd 961: wh-err= 0.999999 th-err= 0.216840 test= nan train= 0.0571429 rnd 962: wh-err= 0.999999 th-err= 0.216839 test= nan train= 0.0571429 rnd 963: wh-err= 0.999999 th-err= 0.216839 test= nan train= 0.0571429 rnd 964: wh-err= 0.999989 th-err= 0.216837 test= nan train= 0.0571429 rnd 965: wh-err= 0.999998 th-err= 0.216836 test= nan train= 0.0571429 rnd 966: wh-err= 0.999999 th-err= 0.216836 test= nan train= 0.0571429 rnd 967: wh-err= 0.999999 th-err= 0.216836 test= nan train= 0.0571429 rnd 968: wh-err= 0.999952 th-err= 0.216825 test= nan train= 0.0571429 rnd 969: wh-err= 0.999963 th-err= 0.216818 test= nan train= 0.0571429 rnd 970: wh-err= 0.999934 th-err= 0.216803 test= nan train= 0.0571429 rnd 971: wh-err= 0.999939 th-err= 0.216790 test= nan train= 0.0571429 rnd 972: wh-err= 0.999971 th-err= 0.216784 test= nan train= 0.0571429 rnd 973: wh-err= 0.999987 th-err= 0.216781 test= nan train= 0.0571429 rnd 974: wh-err= 0.999904 th-err= 0.216760 test= nan train= 0.0571429 rnd 975: wh-err= 0.999851 th-err= 0.216728 test= nan train= 0.0571429 rnd 976: wh-err= 0.999885 th-err= 0.216703 test= nan train= 0.0571429 rnd 977: wh-err= 0.999914 th-err= 0.216684 test= nan train= 0.0571429 rnd 978: wh-err= 0.999965 th-err= 0.216677 test= nan train= 0.0571429 rnd 979: wh-err= 0.999988 th-err= 0.216674 test= nan train= 0.0571429 rnd 980: wh-err= 0.999972 th-err= 0.216668 test= nan train= 0.0571429 rnd 981: wh-err= 0.999988 th-err= 0.216665 test= nan train= 0.0571429 rnd 982: wh-err= 0.999999 th-err= 0.216665 test= nan train= 0.0571429 rnd 983: wh-err= 0.999999 th-err= 0.216665 test= nan train= 0.0571429 rnd 984: wh-err= 0.999999 th-err= 0.216664 test= nan train= 0.0571429 rnd 985: wh-err= 0.999999 th-err= 0.216664 test= nan train= 0.0571429 rnd 986: wh-err= 0.999989 th-err= 0.216662 test= nan train= 0.0571429 rnd 987: wh-err= 0.999999 th-err= 0.216661 test= nan train= 0.0571429 rnd 988: wh-err= 0.999999 th-err= 0.216661 test= nan train= 0.0571429 rnd 989: wh-err= 0.999999 th-err= 0.216661 test= nan train= 0.0571429 rnd 990: wh-err= 0.999999 th-err= 0.216661 test= nan train= 0.0571429 rnd 991: wh-err= 0.999999 th-err= 0.216660 test= nan train= 0.0571429 rnd 992: wh-err= 0.999999 th-err= 0.216660 test= nan train= 0.0571429 rnd 993: wh-err= 0.999989 th-err= 0.216658 test= nan train= 0.0571429 rnd 994: wh-err= 0.999999 th-err= 0.216657 test= nan train= 0.0571429 rnd 995: wh-err= 0.999999 th-err= 0.216657 test= nan train= 0.0571429 rnd 996: wh-err= 0.999999 th-err= 0.216657 test= nan train= 0.0571429 rnd 997: wh-err= 0.999999 th-err= 0.216657 test= nan train= 0.0571429 rnd 998: wh-err= 0.999999 th-err= 0.216656 test= nan train= 0.0571429 rnd 999: wh-err= 0.999999 th-err= 0.216656 test= nan train= 0.0571429 rnd 1000: wh-err= 0.999990 th-err= 0.216654 test= nan train= 0.0571429 === END program1: ./run learn ../dataset2/train --- OK [0s] ===== MAIN: predict/evaluate on train data ===== === START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in === END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out Copyright 2001 AT&T. All rights reserved. Test error = 70.000000 / 70 = 1.000000 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s] ===== MAIN: predict/evaluate on test data ===== === START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in === END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out Copyright 2001 AT&T. All rights reserved. Test error = 30.000000 / 30 = 1.000000 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s] real 0m0.980s user 0m0.296s sys 0m0.068s
Run specification 
Results
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