 | Run 12294 |
| Creator | yorinasub17 |
| Program | boostexter |
| Dataset | Synthetic 10% Density, Small, Few Labels |
| Task type | MulticlassClassification |
| Created | 2y2d ago |
| Download | Login required! |
Done! 
| 7s | |
| 33M | |
| MulticlassClassification | |
| 0 | |
| 0.002 | |
Log file
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0.000000 test= nan train= 0.0000000 rnd 904: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 905: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 906: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 907: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 908: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 909: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 910: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 911: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 912: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 913: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 914: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 915: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 916: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 917: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 918: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 919: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 920: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 921: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 922: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 923: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 924: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 925: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 926: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 927: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 928: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 929: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 930: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 931: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 932: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 933: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 934: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 935: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 936: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 937: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 938: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 939: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 940: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 941: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 942: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 943: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 944: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 945: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 946: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 947: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 948: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 949: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 950: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 951: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 952: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 953: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 954: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 955: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 956: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 957: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 958: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 959: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 960: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 961: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 962: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 963: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 964: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 965: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 966: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 967: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 968: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 969: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 970: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 971: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 972: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 973: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 974: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 975: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 976: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 977: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 978: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 979: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 980: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 981: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 982: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 983: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 984: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 985: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 986: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 987: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 988: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 989: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 990: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 rnd 991: wh-err= 0.679414 th-err= 0.000000 test= nan train= 0.0000000 rnd 992: wh-err= 0.677222 th-err= 0.000000 test= nan train= 0.0000000 rnd 993: wh-err= 0.675808 th-err= 0.000000 test= nan train= 0.0000000 rnd 994: wh-err= 0.679502 th-err= 0.000000 test= nan train= 0.0000000 rnd 995: wh-err= 0.677821 th-err= 0.000000 test= nan train= 0.0000000 rnd 996: wh-err= 0.677188 th-err= 0.000000 test= nan train= 0.0000000 rnd 997: wh-err= 0.693850 th-err= 0.000000 test= nan train= 0.0000000 rnd 998: wh-err= 0.692034 th-err= 0.000000 test= nan train= 0.0000000 rnd 999: wh-err= 0.690796 th-err= 0.000000 test= nan train= 0.0000000 rnd 1000: wh-err= 0.689727 th-err= 0.000000 test= nan train= 0.0000000 === END program1: ./run learn ../dataset2/train --- OK [9s] ===== 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 = 5250.000000 / 5250 = 1.000000 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s] === 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 = 2250.000000 / 2250 = 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 0m10.813s user 0m3.584s sys 0m0.116s
Run specification 
Results
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