 | Run 14329 |
| Creator | internal |
| Program | minimalist-boost |
| Dataset | SleepdataMulti |
| Task type | MulticlassClassification |
| Created | 28d17h ago |
Done! 
| 6s | |
| 66M | |
| MulticlassClassification | |
| 3s | |
| 0.477 | |
| 0s | |
| 0.519 | |
| 0s |
Log file
... (lines omitted) ... iteration:622 feature:81 threshold:2.5 min-objective:0.999709 iteration:623 feature:118 threshold:3.5 min-objective:0.999702 iteration:624 feature:122 threshold:4.5 min-objective:0.99965 iteration:625 feature:58 threshold:2.5 min-objective:0.999696 iteration:626 feature:119 threshold:3.5 min-objective:0.9997 iteration:627 feature:19 threshold:1.5 min-objective:0.999693 iteration:628 feature:133 threshold:2.5 min-objective:0.999709 iteration:629 feature:122 threshold:3.5 min-objective:0.999715 iteration:630 feature:123 threshold:2.5 min-objective:0.999694 iteration:631 feature:103 threshold:2.5 min-objective:0.999674 iteration:632 feature:20 threshold:3.5 min-objective:0.999686 iteration:633 feature:81 threshold:4.5 min-objective:0.999678 iteration:634 feature:137 threshold:3.5 min-objective:0.999691 iteration:635 feature:107 threshold:4.5 min-objective:0.999688 iteration:636 feature:34 threshold:4.5 min-objective:0.999648 iteration:637 feature:33 threshold:3.5 min-objective:0.999688 iteration:638 feature:23 threshold:4.5 min-objective:0.999644 iteration:639 feature:41 threshold:3.5 min-objective:0.999684 iteration:640 feature:138 threshold:4.5 min-objective:0.999691 iteration:641 feature:64 threshold:4.5 min-objective:0.999684 iteration:642 feature:46 threshold:2.5 min-objective:0.999701 iteration:643 feature:81 threshold:4.5 min-objective:0.999708 iteration:644 feature:2 threshold:3.5 min-objective:0.999709 iteration:645 feature:22 threshold:4.5 min-objective:0.999689 iteration:646 feature:8 threshold:2.5 min-objective:0.999716 iteration:647 feature:25 threshold:2.5 min-objective:0.999721 iteration:648 feature:102 threshold:3.5 min-objective:0.999718 iteration:649 feature:5 threshold:4.5 min-objective:0.999682 iteration:650 feature:117 threshold:1.5 min-objective:0.999705 iteration:651 feature:7 threshold:2.5 min-objective:0.999708 iteration:652 feature:69 threshold:3.5 min-objective:0.999711 iteration:653 feature:136 threshold:4.5 min-objective:0.999719 iteration:654 feature:126 threshold:1.5 min-objective:0.99972 iteration:655 feature:31 threshold:4.5 min-objective:0.999733 iteration:656 feature:120 threshold:4.5 min-objective:0.999678 iteration:657 feature:3 threshold:3.5 min-objective:0.999694 iteration:658 feature:61 threshold:2.5 min-objective:0.999699 iteration:659 feature:127 threshold:2.5 min-objective:0.999702 iteration:660 feature:32 threshold:3.5 min-objective:0.999717 iteration:661 feature:14 threshold:2.5 min-objective:0.999704 iteration:662 feature:70 threshold:3.5 min-objective:0.999724 iteration:663 feature:23 threshold:4.5 min-objective:0.999726 iteration:664 feature:119 threshold:3.5 min-objective:0.999732 iteration:665 feature:40 threshold:2.5 min-objective:0.999734 iteration:666 feature:133 threshold:2.5 min-objective:0.999734 iteration:667 feature:6 threshold:3.5 min-objective:0.999741 iteration:668 feature:48 threshold:3.5 min-objective:0.999738 iteration:669 feature:25 threshold:3.5 min-objective:0.99974 iteration:670 feature:141 threshold:4.5 min-objective:0.999734 iteration:671 feature:14 threshold:2.5 min-objective:0.999742 iteration:672 feature:122 threshold:4.5 min-objective:0.999751 iteration:673 feature:44 threshold:2.5 min-objective:0.999711 iteration:674 feature:15 threshold:3.5 min-objective:0.999752 iteration:675 feature:102 threshold:2.5 min-objective:0.999758 iteration:676 feature:133 threshold:2.5 min-objective:0.999743 iteration:677 feature:129 threshold:1.5 min-objective:0.999745 iteration:678 feature:56 threshold:4.5 min-objective:0.999648 iteration:679 feature:107 threshold:3.5 min-objective:0.999694 iteration:680 feature:30 threshold:1.5 min-objective:0.999732 iteration:681 feature:123 threshold:3.5 min-objective:0.999721 iteration:682 feature:120 threshold:3.5 min-objective:0.999721 iteration:683 feature:31 threshold:4.5 min-objective:0.999719 iteration:684 feature:24 threshold:1.5 min-objective:0.999719 iteration:685 feature:131 threshold:1.5 min-objective:0.999733 iteration:686 feature:110 threshold:3.5 min-objective:0.999743 iteration:687 feature:121 threshold:2.5 min-objective:0.999734 iteration:688 feature:135 threshold:4.5 min-objective:0.999744 iteration:689 feature:133 threshold:2.5 min-objective:0.99973 iteration:690 feature:52 threshold:3.5 min-objective:0.999738 iteration:691 feature:46 threshold:2.5 min-objective:0.999749 iteration:692 feature:18 threshold:4.5 min-objective:0.999756 iteration:693 feature:22 threshold:1.5 min-objective:0.999755 iteration:694 feature:88 threshold:3.5 min-objective:0.999753 iteration:695 feature:27 threshold:4.5 min-objective:0.999762 iteration:696 feature:58 threshold:2.5 min-objective:0.999737 iteration:697 feature:122 threshold:3.5 min-objective:0.999746 iteration:698 feature:107 threshold:2.5 min-objective:0.999731 iteration:699 feature:70 threshold:3.5 min-objective:0.999707 iteration:700 feature:8 threshold:3.5 min-objective:0.999744 iteration:701 feature:34 threshold:4.5 min-objective:0.999758 iteration:702 feature:113 threshold:2.5 min-objective:0.999763 iteration:703 feature:12 threshold:3.5 min-objective:0.999769 iteration:704 feature:19 threshold:1.5 min-objective:0.999747 iteration:705 feature:26 threshold:3.5 min-objective:0.999749 iteration:706 feature:46 threshold:2.5 min-objective:0.999776 iteration:707 feature:28 threshold:4.5 min-objective:0.999777 iteration:708 feature:111 threshold:1.5 min-objective:0.999774 iteration:709 feature:89 threshold:3.5 min-objective:0.999777 iteration:710 feature:2 threshold:3.5 min-objective:0.999778 iteration:711 feature:131 threshold:3.5 min-objective:0.999764 iteration:712 feature:81 threshold:2.5 min-objective:0.999759 iteration:713 feature:20 threshold:3.5 min-objective:0.999766 iteration:714 feature:9 threshold:2.5 min-objective:0.999769 iteration:715 feature:65 threshold:2.5 min-objective:0.999768 iteration:716 feature:55 threshold:3.5 min-objective:0.999753 iteration:717 feature:119 threshold:3.5 min-objective:0.99976 iteration:718 feature:105 threshold:3.5 min-objective:0.999773 iteration:719 feature:110 threshold:3.5 min-objective:0.999766 iteration:720 feature:120 threshold:4.5 min-objective:0.999777 iteration:721 feature:123 threshold:1.5 min-objective:0.999745 iteration:722 feature:33 threshold:3.5 min-objective:0.999705 iteration:723 feature:125 threshold:2.5 min-objective:0.999751 iteration:724 feature:110 threshold:3.5 min-objective:0.999767 iteration:725 feature:15 threshold:3.5 min-objective:0.999774 iteration:726 feature:69 threshold:3.5 min-objective:0.999777 iteration:727 feature:23 threshold:4.5 min-objective:0.999775 iteration:728 feature:81 threshold:2.5 min-objective:0.999781 iteration:729 feature:118 threshold:1.5 min-objective:0.999764 iteration:730 feature:137 threshold:2.5 min-objective:0.999764 iteration:731 feature:81 threshold:2.5 min-objective:0.999774 iteration:732 feature:34 threshold:4.5 min-objective:0.999777 iteration:733 feature:64 threshold:4.5 min-objective:0.999782 iteration:734 feature:128 threshold:4.5 min-objective:0.999794 iteration:735 feature:107 threshold:4.5 min-objective:0.999762 iteration:736 feature:25 threshold:3.5 min-objective:0.999761 iteration:737 feature:102 threshold:1.5 min-objective:0.99977 iteration:738 feature:35 threshold:1.5 min-objective:0.999726 iteration:739 feature:6 threshold:3.5 min-objective:0.999766 iteration:740 feature:54 threshold:1.5 min-objective:0.99977 iteration:741 feature:48 threshold:3.5 min-objective:0.999776 iteration:742 feature:19 threshold:3.5 min-objective:0.999783 iteration:743 feature:121 threshold:2.5 min-objective:0.999776 iteration:744 feature:63 threshold:4.5 min-objective:0.999782 iteration:745 feature:15 threshold:3.5 min-objective:0.99979 iteration:746 feature:37 threshold:2.5 min-objective:0.999792 iteration:747 feature:131 threshold:2.5 min-objective:0.999791 iteration:748 feature:138 threshold:2.5 min-objective:0.999782 iteration:749 feature:119 threshold:4.5 min-objective:0.999786 iteration:750 feature:8 threshold:3.5 min-objective:0.999788 iteration:751 feature:123 threshold:2.5 min-objective:0.999792 iteration:752 feature:120 threshold:2.5 min-objective:0.999777 iteration:753 feature:64 threshold:4.5 min-objective:0.999789 iteration:754 feature:133 threshold:3.5 min-objective:0.999791 iteration:755 feature:31 threshold:2.5 min-objective:0.999791 iteration:756 feature:9 threshold:2.5 min-objective:0.999785 iteration:757 feature:133 threshold:2.5 min-objective:0.999801 iteration:758 feature:139 threshold:2.5 min-objective:0.999806 iteration:759 feature:61 threshold:2.5 min-objective:0.999798 iteration:760 feature:52 threshold:3.5 min-objective:0.99979 iteration:761 feature:18 threshold:1.5 min-objective:0.999798 iteration:762 feature:64 threshold:4.5 min-objective:0.999807 iteration:763 feature:119 threshold:3.5 min-objective:0.999807 iteration:764 feature:15 threshold:3.5 min-objective:0.999808 iteration:765 feature:53 threshold:1.5 min-objective:0.999809 iteration:766 feature:2 threshold:2.5 min-objective:0.999792 iteration:767 feature:116 threshold:3.5 min-objective:0.9998 iteration:768 feature:122 threshold:4.5 min-objective:0.999811 iteration:769 feature:123 threshold:4.5 min-objective:0.99976 iteration:770 feature:81 threshold:2.5 min-objective:0.999794 iteration:771 feature:26 threshold:2.5 min-objective:0.999802 iteration:772 feature:133 threshold:3.5 min-objective:0.999811 iteration:773 feature:106 threshold:3.5 min-objective:0.999812 iteration:774 feature:37 threshold:3.5 min-objective:0.999807 iteration:775 feature:102 threshold:1.5 min-objective:0.99981 iteration:776 feature:65 threshold:2.5 min-objective:0.999799 iteration:777 feature:27 threshold:4.5 min-objective:0.999807 iteration:778 feature:120 threshold:3.5 min-objective:0.999786 iteration:779 feature:6 threshold:3.5 min-objective:0.999794 iteration:780 feature:118 threshold:1.5 min-objective:0.999802 iteration:781 feature:30 threshold:2.5 min-objective:0.99978 iteration:782 feature:31 threshold:4.5 min-objective:0.999791 iteration:783 feature:63 threshold:4.5 min-objective:0.999787 iteration:784 feature:19 threshold:1.5 min-objective:0.999807 iteration:785 feature:22 threshold:4.5 min-objective:0.999802 iteration:786 feature:119 threshold:3.5 min-objective:0.999803 iteration:787 feature:88 threshold:3.5 min-objective:0.999804 iteration:788 feature:110 threshold:3.5 min-objective:0.999805 iteration:789 feature:105 threshold:3.5 min-objective:0.999812 iteration:790 feature:15 threshold:3.5 min-objective:0.999806 iteration:791 feature:121 threshold:2.5 min-objective:0.999811 iteration:792 feature:11 threshold:4.5 min-objective:0.999815 iteration:793 feature:133 threshold:3.5 min-objective:0.999817 iteration:794 feature:53 threshold:1.5 min-objective:0.999816 iteration:795 feature:3 threshold:3.5 min-objective:0.999815 iteration:796 feature:139 threshold:3.5 min-objective:0.999813 iteration:797 feature:133 threshold:3.5 min-objective:0.999812 iteration:798 feature:116 threshold:3.5 min-objective:0.99982 iteration:799 feature:22 threshold:4.5 min-objective:0.99982 iteration:800 feature:107 threshold:2.5 min-objective:0.999813 iteration:801 feature:56 threshold:3.5 min-objective:0.999781 iteration:802 feature:120 threshold:4.5 min-objective:0.999806 iteration:803 feature:123 threshold:3.5 min-objective:0.999793 iteration:804 feature:21 threshold:3.5 min-objective:0.999781 iteration:805 feature:123 threshold:2.5 min-objective:0.999799 iteration:806 feature:88 threshold:3.5 min-objective:0.999806 iteration:807 feature:46 threshold:2.5 min-objective:0.999808 iteration:808 feature:133 threshold:3.5 min-objective:0.999812 iteration:809 feature:15 threshold:3.5 min-objective:0.999817 iteration:810 feature:22 threshold:4.5 min-objective:0.999818 iteration:811 feature:54 threshold:1.5 min-objective:0.999821 iteration:812 feature:25 threshold:2.5 min-objective:0.999822 iteration:813 feature:126 threshold:1.5 min-objective:0.999807 iteration:814 feature:89 threshold:3.5 min-objective:0.999825 iteration:815 feature:133 threshold:3.5 min-objective:0.999826 iteration:816 feature:45 threshold:2.5 min-objective:0.999825 iteration:817 feature:116 threshold:3.5 min-objective:0.999825 iteration:818 feature:101 threshold:3.5 min-objective:0.999831 iteration:819 feature:34 threshold:4.5 min-objective:0.999827 iteration:820 feature:110 threshold:3.5 min-objective:0.999827 iteration:821 feature:31 threshold:4.5 min-objective:0.99983 iteration:822 feature:18 threshold:3.5 min-objective:0.999829 iteration:823 feature:2 threshold:2.5 min-objective:0.999831 iteration:824 feature:60 threshold:1.5 min-objective:0.999822 iteration:825 feature:120 threshold:1.5 min-objective:0.99983 iteration:826 feature:32 threshold:4.5 min-objective:0.999805 iteration:827 feature:102 threshold:4.5 min-objective:0.999818 iteration:828 feature:65 threshold:2.5 min-objective:0.999821 iteration:829 feature:38 threshold:2.5 min-objective:0.999822 iteration:830 feature:22 threshold:1.5 min-objective:0.999823 iteration:831 feature:107 threshold:3.5 min-objective:0.999819 iteration:832 feature:61 threshold:2.5 min-objective:0.999816 iteration:833 feature:6 threshold:3.5 min-objective:0.999827 iteration:834 feature:29 threshold:2.5 min-objective:0.999832 iteration:835 feature:119 threshold:3.5 min-objective:0.999834 iteration:836 feature:3 threshold:3.5 min-objective:0.999831 iteration:837 feature:33 threshold:3.5 min-objective:0.999829 iteration:838 feature:88 threshold:3.5 min-objective:0.999825 iteration:839 feature:110 threshold:3.5 min-objective:0.999828 iteration:840 feature:117 threshold:1.5 min-objective:0.999835 iteration:841 feature:15 threshold:3.5 min-objective:0.999829 iteration:842 feature:7 threshold:2.5 min-objective:0.999835 iteration:843 feature:9 threshold:2.5 min-objective:0.999838 iteration:844 feature:133 threshold:2.5 min-objective:0.99984 iteration:845 feature:6 threshold:3.5 min-objective:0.999842 iteration:846 feature:111 threshold:1.5 min-objective:0.999843 iteration:847 feature:122 threshold:4.5 min-objective:0.999836 iteration:848 feature:35 threshold:1.5 min-objective:0.999833 iteration:849 feature:69 threshold:3.5 min-objective:0.999832 iteration:850 feature:24 threshold:1.5 min-objective:0.999842 iteration:851 feature:8 threshold:2.5 min-objective:0.999842 iteration:852 feature:28 threshold:4.5 min-objective:0.999839 iteration:853 feature:140 threshold:2.5 min-objective:0.999845 iteration:854 feature:15 threshold:3.5 min-objective:0.999844 iteration:855 feature:110 threshold:3.5 min-objective:0.999846 iteration:856 feature:133 threshold:2.5 min-objective:0.999847 iteration:857 feature:116 threshold:3.5 min-objective:0.999847 iteration:858 feature:17 threshold:3.5 min-objective:0.999849 iteration:859 feature:81 threshold:2.5 min-objective:0.99985 iteration:860 feature:128 threshold:3.5 min-objective:0.999851 iteration:861 feature:129 threshold:3.5 min-objective:0.999833 iteration:862 feature:53 threshold:1.5 min-objective:0.999805 iteration:863 feature:127 threshold:2.5 min-objective:0.999833 iteration:864 feature:133 threshold:2.5 min-objective:0.999829 iteration:865 feature:63 threshold:4.5 min-objective:0.999839 iteration:866 feature:133 threshold:2.5 min-objective:0.99984 iteration:867 feature:31 threshold:4.5 min-objective:0.999843 iteration:868 feature:34 threshold:4.5 min-objective:0.999844 iteration:869 feature:88 threshold:4.5 min-objective:0.999845 iteration:870 feature:111 threshold:1.5 min-objective:0.999841 iteration:871 feature:122 threshold:3.5 min-objective:0.999845 iteration:872 feature:64 threshold:4.5 min-objective:0.999833 iteration:873 feature:19 threshold:1.5 min-objective:0.999841 iteration:874 feature:28 threshold:4.5 min-objective:0.999849 iteration:875 feature:115 threshold:3.5 min-objective:0.999851 iteration:876 feature:20 threshold:4.5 min-objective:0.999851 iteration:877 feature:118 threshold:1.5 min-objective:0.99983 iteration:878 feature:135 threshold:3.5 min-objective:0.99984 iteration:879 feature:63 threshold:4.5 min-objective:0.99984 iteration:880 feature:107 threshold:2.5 min-objective:0.999841 iteration:881 feature:11 threshold:2.5 min-objective:0.999832 iteration:882 feature:58 threshold:2.5 min-objective:0.999836 iteration:883 feature:81 threshold:2.5 min-objective:0.999838 iteration:884 feature:58 threshold:2.5 min-objective:0.999846 iteration:885 feature:47 threshold:2.5 min-objective:0.99985 iteration:886 feature:103 threshold:2.5 min-objective:0.999849 iteration:887 feature:126 threshold:1.5 min-objective:0.999853 iteration:888 feature:28 threshold:4.5 min-objective:0.999853 iteration:889 feature:81 threshold:2.5 min-objective:0.999852 iteration:890 feature:45 threshold:2.5 min-objective:0.999854 iteration:891 feature:15 threshold:3.5 min-objective:0.999855 iteration:892 feature:23 threshold:3.5 min-objective:0.999856 iteration:893 feature:123 threshold:3.5 min-objective:0.999857 iteration:894 feature:102 threshold:2.5 min-objective:0.999832 iteration:895 feature:54 threshold:1.5 min-objective:0.999843 iteration:896 feature:110 threshold:3.5 min-objective:0.999848 iteration:897 feature:22 threshold:4.5 min-objective:0.999852 iteration:898 feature:40 threshold:1.5 min-objective:0.999855 iteration:899 feature:140 threshold:1.5 min-objective:0.999857 iteration:900 feature:10 threshold:1.5 min-objective:0.999845 iteration:901 feature:123 threshold:2.5 min-objective:0.99984 iteration:902 feature:137 threshold:3.5 min-objective:0.999843 iteration:903 feature:55 threshold:3.5 min-objective:0.999849 iteration:904 feature:119 threshold:3.5 min-objective:0.99985 iteration:905 feature:47 threshold:2.5 min-objective:0.999854 iteration:906 feature:45 threshold:2.5 min-objective:0.999856 iteration:907 feature:133 threshold:3.5 min-objective:0.999855 iteration:908 feature:113 threshold:3.5 min-objective:0.999857 iteration:909 feature:56 threshold:4.5 min-objective:0.999855 iteration:910 feature:129 threshold:1.5 min-objective:0.999816 iteration:911 feature:120 threshold:4.5 min-objective:0.999828 iteration:912 feature:3 threshold:2.5 min-objective:0.999838 iteration:913 feature:81 threshold:2.5 min-objective:0.999842 iteration:914 feature:79 threshold:1.5 min-objective:0.999846 iteration:915 feature:107 threshold:2.5 min-objective:0.999848 iteration:916 feature:20 threshold:4.5 min-objective:0.999846 iteration:917 feature:60 threshold:1.5 min-objective:0.99985 iteration:918 feature:54 threshold:1.5 min-objective:0.999852 iteration:919 feature:133 threshold:3.5 min-objective:0.999855 iteration:920 feature:4 threshold:4.5 min-objective:0.999856 iteration:921 feature:93 threshold:3.5 min-objective:0.999857 iteration:922 feature:45 threshold:2.5 min-objective:0.999859 iteration:923 feature:19 threshold:1.5 min-objective:0.999859 iteration:924 feature:34 threshold:4.5 min-objective:0.999858 iteration:925 feature:139 threshold:3.5 min-objective:0.999859 iteration:926 feature:133 threshold:3.5 min-objective:0.999856 iteration:927 feature:31 threshold:4.5 min-objective:0.999862 iteration:928 feature:61 threshold:2.5 min-objective:0.999859 iteration:929 feature:88 threshold:4.5 min-objective:0.999859 iteration:930 feature:21 threshold:2.5 min-objective:0.999866 iteration:931 feature:118 threshold:2.5 min-objective:0.999859 iteration:932 feature:26 threshold:2.5 min-objective:0.999861 iteration:933 feature:72 threshold:1.5 min-objective:0.999862 iteration:934 feature:3 threshold:3.5 min-objective:0.999861 iteration:935 feature:128 threshold:4.5 min-objective:0.999847 iteration:936 feature:40 threshold:1.5 min-objective:0.999846 iteration:937 feature:42 threshold:1.5 min-objective:0.999858 iteration:938 feature:121 threshold:2.5 min-objective:0.999859 iteration:939 feature:15 threshold:3.5 min-objective:0.999859 iteration:940 feature:72 threshold:1.5 min-objective:0.999862 iteration:941 feature:46 threshold:2.5 min-objective:0.999865 iteration:942 feature:105 threshold:3.5 min-objective:0.999865 iteration:943 feature:37 threshold:2.5 min-objective:0.999865 iteration:944 feature:46 threshold:2.5 min-objective:0.999865 iteration:945 feature:14 threshold:2.5 min-objective:0.999868 iteration:946 feature:102 threshold:3.5 min-objective:0.999868 iteration:947 feature:64 threshold:4.5 min-objective:0.99986 iteration:948 feature:86 threshold:3.5 min-objective:0.999863 iteration:949 feature:133 threshold:3.5 min-objective:0.999864 iteration:950 feature:137 threshold:2.5 min-objective:0.999868 iteration:951 feature:43 threshold:4.5 min-objective:0.999862 iteration:952 feature:44 threshold:2.5 min-objective:0.999867 iteration:953 feature:112 threshold:4.5 min-objective:0.999855 iteration:954 feature:69 threshold:3.5 min-objective:0.999867 iteration:955 feature:140 threshold:1.5 min-objective:0.999869 iteration:956 feature:22 threshold:1.5 min-objective:0.999872 iteration:957 feature:105 threshold:3.5 min-objective:0.999864 iteration:958 feature:25 threshold:3.5 min-objective:0.99987 iteration:959 feature:40 threshold:1.5 min-objective:0.999871 iteration:960 feature:136 threshold:4.5 min-objective:0.999872 iteration:961 feature:133 threshold:3.5 min-objective:0.999873 iteration:962 feature:31 threshold:4.5 min-objective:0.999875 iteration:963 feature:125 threshold:3.5 min-objective:0.999868 iteration:964 feature:141 threshold:4.5 min-objective:0.99986 iteration:965 feature:111 threshold:1.5 min-objective:0.999869 iteration:966 feature:121 threshold:2.5 min-objective:0.999874 iteration:967 feature:84 threshold:3.5 min-objective:0.999878 iteration:968 feature:45 threshold:2.5 min-objective:0.99988 iteration:969 feature:133 threshold:3.5 min-objective:0.999879 iteration:970 feature:89 threshold:3.5 min-objective:0.99988 iteration:971 feature:30 threshold:2.5 min-objective:0.99988 iteration:972 feature:123 threshold:2.5 min-objective:0.999854 iteration:973 feature:107 threshold:4.5 min-objective:0.999874 iteration:974 feature:122 threshold:4.5 min-objective:0.999856 iteration:975 feature:27 threshold:4.5 min-objective:0.999871 iteration:976 feature:33 threshold:2.5 min-objective:0.999868 iteration:977 feature:20 threshold:3.5 min-objective:0.999861 iteration:978 feature:2 threshold:2.5 min-objective:0.999864 iteration:979 feature:138 threshold:2.5 min-objective:0.999875 iteration:980 feature:105 threshold:3.5 min-objective:0.999867 iteration:981 feature:64 threshold:4.5 min-objective:0.999869 iteration:982 feature:9 threshold:2.5 min-objective:0.999869 iteration:983 feature:19 threshold:3.5 min-objective:0.999875 iteration:984 feature:120 threshold:4.5 min-objective:0.999865 iteration:985 feature:6 threshold:3.5 min-objective:0.999868 iteration:986 feature:123 threshold:1.5 min-objective:0.999868 iteration:987 feature:56 threshold:4.5 min-objective:0.999875 iteration:988 feature:18 threshold:1.5 min-objective:0.999863 iteration:989 feature:13 threshold:2.5 min-objective:0.999874 iteration:990 feature:34 threshold:4.5 min-objective:0.999877 iteration:991 feature:107 threshold:2.5 min-objective:0.999877 iteration:992 feature:126 threshold:1.5 min-objective:0.999862 iteration:993 feature:45 threshold:2.5 min-objective:0.999872 iteration:994 feature:8 threshold:3.5 min-objective:0.999878 iteration:995 feature:89 threshold:3.5 min-objective:0.99988 iteration:996 feature:46 threshold:2.5 min-objective:0.99988 iteration:997 feature:6 threshold:3.5 min-objective:0.999881 iteration:998 feature:102 threshold:2.5 min-objective:0.999882 iteration:999 feature:110 threshold:3.5 min-objective:0.999866 === END program1: ./run learn ../dataset2/train --- OK [3s] ===== 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 === 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 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s] real 0m11.350s user 0m6.888s sys 0m0.652s
Run specification 
Results
Must be logged in to post comments.