 | Run 7412 |
| Creator | favre |
| Program | miralium |
| Dataset | ner-conll-2002-esp |
| Task type | SequenceTagging |
| Created | 2y225d ago |
| Download | Login required! |
Done! 
| 34s | |
| 1176M | |
| SequenceTagging | |
| 0.959 | |
| 0.744 | |
| 0.942 | |
| 0.616 | |
Log file
===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset2/train read 2 templates from "featureTemplates" counting: 0 counting: 100 counting: 200 counting: 300 counting: 400 counting: 500 counting: 600 counting: 700 counting: 800 counting: 900 counting: 1000 counting: 1100 counting: 1200 counting: 1300 counting: 1400 counting: 1500 counting: 1600 counting: 1700 counting: 1800 counting: 1900 counting: 2000 counting: 2100 counting: 2200 counting: 2300 counting: 2400 counting: 2500 counting: 2600 counting: 2700 counting: 2800 counting: 2900 counting: 3000 counting: 3100 counting: 3200 counting: 3300 counting: 3400 counting: 3500 counting: 3600 counting: 3700 counting: 3800 counting: 3900 counting: 4000 counting: 4100 counting: 4200 counting: 4300 counting: 4400 counting: 4500 counting: 4600 counting: 4700 counting: 4800 counting: 4900 counting: 5000 counting: 5100 counting: 5200 counting: 5300 counting: 5400 counting: 5500 counting: 5600 counting: 5700 counting: 5800 counting: 5900 counting: 6000 counting: 6100 counting: 6200 counting: 6300 counting: 6400 counting: 6500 counting: 6600 counting: 6700 counting: 6800 counting: 6900 counting: 7000 counting: 7100 counting: 7200 counting: 7300 counting: 7400 counting: 7500 counting: 7600 counting: 7700 counting: 7800 counting: 7900 counting: 8000 counting: 8100 counting: 8200 counting: 8300 counting: 8323 unigrams: 26099, bigrams: 1 unigrams: 13491, bigrams: 1, cutoff: 2 labels: 9 model: 121500 weights iteration 0 train: 100 examples, terr=0.2765 fscore=0.7235 train: 200 examples, terr=0.2112 fscore=0.7888 train: 300 examples, terr=0.2159 fscore=0.7841 train: 400 examples, terr=0.2117 fscore=0.7883 train: 500 examples, terr=0.2009 fscore=0.7991 train: 600 examples, terr=0.2010 fscore=0.7990 train: 700 examples, terr=0.1937 fscore=0.8063 train: 800 examples, terr=0.1924 fscore=0.8076 train: 900 examples, terr=0.1902 fscore=0.8098 train: 1000 examples, terr=0.1921 fscore=0.8079 train: 1100 examples, terr=0.1936 fscore=0.8064 train: 1200 examples, terr=0.1885 fscore=0.8115 train: 1300 examples, terr=0.1870 fscore=0.8130 train: 1400 examples, terr=0.1882 fscore=0.8118 train: 1500 examples, terr=0.1883 fscore=0.8117 train: 1600 examples, terr=0.1850 fscore=0.8150 train: 1700 examples, terr=0.1844 fscore=0.8156 train: 1800 examples, terr=0.1842 fscore=0.8158 train: 1900 examples, terr=0.1835 fscore=0.8165 train: 2000 examples, terr=0.1826 fscore=0.8174 train: 2100 examples, terr=0.1811 fscore=0.8189 train: 2200 examples, terr=0.1789 fscore=0.8211 train: 2300 examples, terr=0.1766 fscore=0.8234 train: 2400 examples, terr=0.1760 fscore=0.8240 train: 2500 examples, terr=0.1758 fscore=0.8242 train: 2600 examples, terr=0.1760 fscore=0.8240 train: 2700 examples, terr=0.1749 fscore=0.8251 train: 2800 examples, terr=0.1750 fscore=0.8250 train: 2900 examples, terr=0.1759 fscore=0.8241 train: 3000 examples, terr=0.1746 fscore=0.8254 train: 3100 examples, terr=0.1739 fscore=0.8261 train: 3200 examples, terr=0.1739 fscore=0.8261 train: 3300 examples, terr=0.1740 fscore=0.8260 train: 3400 examples, terr=0.1727 fscore=0.8273 train: 3500 examples, terr=0.1712 fscore=0.8288 train: 3600 examples, terr=0.1699 fscore=0.8301 train: 3700 examples, terr=0.1691 fscore=0.8309 train: 3800 examples, terr=0.1694 fscore=0.8306 train: 3900 examples, terr=0.1691 fscore=0.8309 train: 4000 examples, terr=0.1690 fscore=0.8310 train: 4100 examples, terr=0.1689 fscore=0.8311 train: 4200 examples, terr=0.1680 fscore=0.8320 train: 4300 examples, terr=0.1668 fscore=0.8332 train: 4400 examples, terr=0.1668 fscore=0.8332 train: 4500 examples, terr=0.1666 fscore=0.8334 train: 4600 examples, terr=0.1668 fscore=0.8332 train: 4700 examples, terr=0.1671 fscore=0.8329 train: 4800 examples, terr=0.1676 fscore=0.8324 train: 4900 examples, terr=0.1683 fscore=0.8317 train: 5000 examples, terr=0.1674 fscore=0.8326 train: 5100 examples, terr=0.1666 fscore=0.8334 train: 5200 examples, terr=0.1667 fscore=0.8333 train: 5300 examples, terr=0.1666 fscore=0.8334 train: 5400 examples, terr=0.1656 fscore=0.8344 train: 5500 examples, terr=0.1655 fscore=0.8345 train: 5600 examples, terr=0.1646 fscore=0.8354 train: 5700 examples, terr=0.1645 fscore=0.8355 train: 5800 examples, terr=0.1636 fscore=0.8364 train: 5900 examples, terr=0.1635 fscore=0.8365 train: 6000 examples, terr=0.1629 fscore=0.8371 train: 6100 examples, terr=0.1626 fscore=0.8374 train: 6200 examples, terr=0.1618 fscore=0.8382 train: 6300 examples, terr=0.1615 fscore=0.8385 train: 6400 examples, terr=0.1615 fscore=0.8385 train: 6500 examples, terr=0.1613 fscore=0.8387 train: 6600 examples, terr=0.1613 fscore=0.8387 train: 6700 examples, terr=0.1610 fscore=0.8390 train: 6800 examples, terr=0.1621 fscore=0.8379 train: 6900 examples, terr=0.1624 fscore=0.8376 train: 7000 examples, terr=0.1613 fscore=0.8387 train: 7100 examples, terr=0.1607 fscore=0.8393 train: 7200 examples, terr=0.1603 fscore=0.8397 train: 7300 examples, terr=0.1598 fscore=0.8402 train: 7400 examples, terr=0.1600 fscore=0.8400 train: 7500 examples, terr=0.1597 fscore=0.8403 train: 7600 examples, terr=0.1591 fscore=0.8409 train: 7700 examples, terr=0.1591 fscore=0.8409 train: 7800 examples, terr=0.1582 fscore=0.8418 train: 7900 examples, terr=0.1582 fscore=0.8418 train: 8000 examples, terr=0.1579 fscore=0.8421 train: 8100 examples, terr=0.1579 fscore=0.8421 train: 8200 examples, terr=0.1582 fscore=0.8418 train: 8300 examples, terr=0.1584 fscore=0.8416 train: 8323 examples, terr=0.1582 fscore=0.8418 iteration 1 train: 100 examples, terr=0.1460 fscore=0.8540 train: 200 examples, terr=0.0986 fscore=0.9014 train: 300 examples, terr=0.1050 fscore=0.8950 train: 400 examples, terr=0.1049 fscore=0.8951 train: 500 examples, terr=0.0988 fscore=0.9012 train: 600 examples, terr=0.1002 fscore=0.8998 train: 700 examples, terr=0.1010 fscore=0.8990 train: 800 examples, terr=0.0994 fscore=0.9006 train: 900 examples, terr=0.0989 fscore=0.9011 train: 1000 examples, terr=0.1014 fscore=0.8986 train: 1100 examples, terr=0.1038 fscore=0.8962 train: 1200 examples, terr=0.1009 fscore=0.8991 train: 1300 examples, terr=0.1000 fscore=0.9000 train: 1400 examples, terr=0.1038 fscore=0.8962 train: 1500 examples, terr=0.1032 fscore=0.8968 train: 1600 examples, terr=0.1018 fscore=0.8982 train: 1700 examples, terr=0.1025 fscore=0.8975 train: 1800 examples, terr=0.1024 fscore=0.8976 train: 1900 examples, terr=0.1023 fscore=0.8977 train: 2000 examples, terr=0.1009 fscore=0.8991 train: 2100 examples, terr=0.1004 fscore=0.8996 train: 2200 examples, terr=0.0982 fscore=0.9018 train: 2300 examples, terr=0.0971 fscore=0.9029 train: 2400 examples, terr=0.0977 fscore=0.9023 train: 2500 examples, terr=0.0994 fscore=0.9006 train: 2600 examples, terr=0.0998 fscore=0.9002 train: 2700 examples, terr=0.0990 fscore=0.9010 train: 2800 examples, terr=0.0996 fscore=0.9004 train: 2900 examples, terr=0.1015 fscore=0.8985 train: 3000 examples, terr=0.1010 fscore=0.8990 train: 3100 examples, terr=0.1011 fscore=0.8989 train: 3200 examples, terr=0.1018 fscore=0.8982 train: 3300 examples, terr=0.1024 fscore=0.8976 train: 3400 examples, terr=0.1015 fscore=0.8985 train: 3500 examples, terr=0.1006 fscore=0.8994 train: 3600 examples, terr=0.0996 fscore=0.9004 train: 3700 examples, terr=0.0995 fscore=0.9005 train: 3800 examples, terr=0.0998 fscore=0.9002 train: 3900 examples, terr=0.1002 fscore=0.8998 train: 4000 examples, terr=0.1003 fscore=0.8997 train: 4100 examples, terr=0.1000 fscore=0.9000 train: 4200 examples, terr=0.1001 fscore=0.8999 train: 4300 examples, terr=0.1000 fscore=0.9000 train: 4400 examples, terr=0.1003 fscore=0.8997 train: 4500 examples, terr=0.1028 fscore=0.8972 train: 4600 examples, terr=0.1032 fscore=0.8968 train: 4700 examples, terr=0.1037 fscore=0.8963 train: 4800 examples, terr=0.1042 fscore=0.8958 train: 4900 examples, terr=0.1053 fscore=0.8947 train: 5000 examples, terr=0.1052 fscore=0.8948 train: 5100 examples, terr=0.1045 fscore=0.8955 train: 5200 examples, terr=0.1050 fscore=0.8950 train: 5300 examples, terr=0.1048 fscore=0.8952 train: 5400 examples, terr=0.1046 fscore=0.8954 train: 5500 examples, terr=0.1047 fscore=0.8953 train: 5600 examples, terr=0.1043 fscore=0.8957 train: 5700 examples, terr=0.1050 fscore=0.8950 train: 5800 examples, terr=0.1046 fscore=0.8954 train: 5900 examples, terr=0.1046 fscore=0.8954 train: 6000 examples, terr=0.1046 fscore=0.8954 train: 6100 examples, terr=0.1045 fscore=0.8955 train: 6200 examples, terr=0.1042 fscore=0.8958 train: 6300 examples, terr=0.1040 fscore=0.8960 train: 6400 examples, terr=0.1040 fscore=0.8960 train: 6500 examples, terr=0.1036 fscore=0.8964 train: 6600 examples, terr=0.1043 fscore=0.8957 train: 6700 examples, terr=0.1043 fscore=0.8957 train: 6800 examples, terr=0.1049 fscore=0.8951 train: 6900 examples, terr=0.1052 fscore=0.8948 train: 7000 examples, terr=0.1050 fscore=0.8950 train: 7100 examples, terr=0.1048 fscore=0.8952 train: 7200 examples, terr=0.1048 fscore=0.8952 train: 7300 examples, terr=0.1049 fscore=0.8951 train: 7400 examples, terr=0.1054 fscore=0.8946 train: 7500 examples, terr=0.1055 fscore=0.8945 train: 7600 examples, terr=0.1056 fscore=0.8944 train: 7700 examples, terr=0.1060 fscore=0.8940 train: 7800 examples, terr=0.1057 fscore=0.8943 train: 7900 examples, terr=0.1059 fscore=0.8941 train: 8000 examples, terr=0.1057 fscore=0.8943 train: 8100 examples, terr=0.1058 fscore=0.8942 train: 8200 examples, terr=0.1061 fscore=0.8939 train: 8300 examples, terr=0.1063 fscore=0.8937 train: 8323 examples, terr=0.1062 fscore=0.8938 iteration 2 train: 100 examples, terr=0.1424 fscore=0.8576 train: 200 examples, terr=0.0890 fscore=0.9110 train: 300 examples, terr=0.0896 fscore=0.9104 train: 400 examples, terr=0.0914 fscore=0.9086 train: 500 examples, terr=0.0883 fscore=0.9117 train: 600 examples, terr=0.0896 fscore=0.9104 train: 700 examples, terr=0.0905 fscore=0.9095 train: 800 examples, terr=0.0889 fscore=0.9111 train: 900 examples, terr=0.0851 fscore=0.9149 train: 1000 examples, terr=0.0868 fscore=0.9132 train: 1100 examples, terr=0.0870 fscore=0.9130 train: 1200 examples, terr=0.0828 fscore=0.9172 train: 1300 examples, terr=0.0821 fscore=0.9179 train: 1400 examples, terr=0.0831 fscore=0.9169 train: 1500 examples, terr=0.0830 fscore=0.9170 train: 1600 examples, terr=0.0812 fscore=0.9188 train: 1700 examples, terr=0.0803 fscore=0.9197 train: 1800 examples, terr=0.0801 fscore=0.9199 train: 1900 examples, terr=0.0803 fscore=0.9197 train: 2000 examples, terr=0.0799 fscore=0.9201 train: 2100 examples, terr=0.0791 fscore=0.9209 train: 2200 examples, terr=0.0775 fscore=0.9225 train: 2300 examples, terr=0.0759 fscore=0.9241 train: 2400 examples, terr=0.0760 fscore=0.9240 train: 2500 examples, terr=0.0780 fscore=0.9220 train: 2600 examples, terr=0.0784 fscore=0.9216 train: 2700 examples, terr=0.0779 fscore=0.9221 train: 2800 examples, terr=0.0776 fscore=0.9224 train: 2900 examples, terr=0.0798 fscore=0.9202 train: 3000 examples, terr=0.0793 fscore=0.9207 train: 3100 examples, terr=0.0791 fscore=0.9209 train: 3200 examples, terr=0.0796 fscore=0.9204 train: 3300 examples, terr=0.0801 fscore=0.9199 train: 3400 examples, terr=0.0794 fscore=0.9206 train: 3500 examples, terr=0.0789 fscore=0.9211 train: 3600 examples, terr=0.0783 fscore=0.9217 train: 3700 examples, terr=0.0784 fscore=0.9216 train: 3800 examples, terr=0.0790 fscore=0.9210 train: 3900 examples, terr=0.0793 fscore=0.9207 train: 4000 examples, terr=0.0799 fscore=0.9201 train: 4100 examples, terr=0.0806 fscore=0.9194 train: 4200 examples, terr=0.0809 fscore=0.9191 train: 4300 examples, terr=0.0812 fscore=0.9188 train: 4400 examples, terr=0.0818 fscore=0.9182 train: 4500 examples, terr=0.0842 fscore=0.9158 train: 4600 examples, terr=0.0841 fscore=0.9159 train: 4700 examples, terr=0.0848 fscore=0.9152 train: 4800 examples, terr=0.0855 fscore=0.9145 train: 4900 examples, terr=0.0861 fscore=0.9139 train: 5000 examples, terr=0.0858 fscore=0.9142 train: 5100 examples, terr=0.0850 fscore=0.9150 train: 5200 examples, terr=0.0851 fscore=0.9149 train: 5300 examples, terr=0.0850 fscore=0.9150 train: 5400 examples, terr=0.0848 fscore=0.9152 train: 5500 examples, terr=0.0850 fscore=0.9150 train: 5600 examples, terr=0.0849 fscore=0.9151 train: 5700 examples, terr=0.0855 fscore=0.9145 train: 5800 examples, terr=0.0852 fscore=0.9148 train: 5900 examples, terr=0.0848 fscore=0.9152 train: 6000 examples, terr=0.0849 fscore=0.9151 train: 6100 examples, terr=0.0850 fscore=0.9150 train: 6200 examples, terr=0.0847 fscore=0.9153 train: 6300 examples, terr=0.0848 fscore=0.9152 train: 6400 examples, terr=0.0850 fscore=0.9150 train: 6500 examples, terr=0.0847 fscore=0.9153 train: 6600 examples, terr=0.0852 fscore=0.9148 train: 6700 examples, terr=0.0850 fscore=0.9150 train: 6800 examples, terr=0.0858 fscore=0.9142 train: 6900 examples, terr=0.0862 fscore=0.9138 train: 7000 examples, terr=0.0860 fscore=0.9140 train: 7100 examples, terr=0.0858 fscore=0.9142 train: 7200 examples, terr=0.0858 fscore=0.9142 train: 7300 examples, terr=0.0861 fscore=0.9139 train: 7400 examples, terr=0.0862 fscore=0.9138 train: 7500 examples, terr=0.0863 fscore=0.9137 train: 7600 examples, terr=0.0865 fscore=0.9135 train: 7700 examples, terr=0.0868 fscore=0.9132 train: 7800 examples, terr=0.0866 fscore=0.9134 train: 7900 examples, terr=0.0867 fscore=0.9133 train: 8000 examples, terr=0.0866 fscore=0.9134 train: 8100 examples, terr=0.0868 fscore=0.9132 train: 8200 examples, terr=0.0871 fscore=0.9129 train: 8300 examples, terr=0.0875 fscore=0.9125 train: 8323 examples, terr=0.0874 fscore=0.9126 iteration 3 train: 100 examples, terr=0.1310 fscore=0.8690 train: 200 examples, terr=0.0750 fscore=0.9250 train: 300 examples, terr=0.0732 fscore=0.9268 train: 400 examples, terr=0.0724 fscore=0.9276 train: 500 examples, terr=0.0705 fscore=0.9295 train: 600 examples, terr=0.0707 fscore=0.9293 train: 700 examples, terr=0.0734 fscore=0.9266 train: 800 examples, terr=0.0737 fscore=0.9263 train: 900 examples, terr=0.0703 fscore=0.9297 train: 1000 examples, terr=0.0702 fscore=0.9298 train: 1100 examples, terr=0.0699 fscore=0.9301 train: 1200 examples, terr=0.0669 fscore=0.9331 train: 1300 examples, terr=0.0674 fscore=0.9326 train: 1400 examples, terr=0.0693 fscore=0.9307 train: 1500 examples, terr=0.0688 fscore=0.9312 train: 1600 examples, terr=0.0676 fscore=0.9324 train: 1700 examples, terr=0.0679 fscore=0.9321 train: 1800 examples, terr=0.0679 fscore=0.9321 train: 1900 examples, terr=0.0685 fscore=0.9315 train: 2000 examples, terr=0.0681 fscore=0.9319 train: 2100 examples, terr=0.0673 fscore=0.9327 train: 2200 examples, terr=0.0665 fscore=0.9335 train: 2300 examples, terr=0.0654 fscore=0.9346 train: 2400 examples, terr=0.0650 fscore=0.9350 train: 2500 examples, terr=0.0661 fscore=0.9339 train: 2600 examples, terr=0.0665 fscore=0.9335 train: 2700 examples, terr=0.0652 fscore=0.9348 train: 2800 examples, terr=0.0659 fscore=0.9341 train: 2900 examples, terr=0.0675 fscore=0.9325 train: 3000 examples, terr=0.0673 fscore=0.9327 train: 3100 examples, terr=0.0679 fscore=0.9321 train: 3200 examples, terr=0.0678 fscore=0.9322 train: 3300 examples, terr=0.0692 fscore=0.9308 train: 3400 examples, terr=0.0692 fscore=0.9308 train: 3500 examples, terr=0.0685 fscore=0.9315 train: 3600 examples, terr=0.0680 fscore=0.9320 train: 3700 examples, terr=0.0679 fscore=0.9321 train: 3800 examples, terr=0.0684 fscore=0.9316 train: 3900 examples, terr=0.0686 fscore=0.9314 train: 4000 examples, terr=0.0694 fscore=0.9306 train: 4100 examples, terr=0.0705 fscore=0.9295 train: 4200 examples, terr=0.0712 fscore=0.9288 train: 4300 examples, terr=0.0711 fscore=0.9289 train: 4400 examples, terr=0.0711 fscore=0.9289 train: 4500 examples, terr=0.0723 fscore=0.9277 train: 4600 examples, terr=0.0728 fscore=0.9272 train: 4700 examples, terr=0.0735 fscore=0.9265 train: 4800 examples, terr=0.0739 fscore=0.9261 train: 4900 examples, terr=0.0746 fscore=0.9254 train: 5000 examples, terr=0.0747 fscore=0.9253 train: 5100 examples, terr=0.0744 fscore=0.9256 train: 5200 examples, terr=0.0749 fscore=0.9251 train: 5300 examples, terr=0.0750 fscore=0.9250 train: 5400 examples, terr=0.0746 fscore=0.9254 train: 5500 examples, terr=0.0748 fscore=0.9252 train: 5600 examples, terr=0.0747 fscore=0.9253 train: 5700 examples, terr=0.0756 fscore=0.9244 train: 5800 examples, terr=0.0754 fscore=0.9246 train: 5900 examples, terr=0.0751 fscore=0.9249 train: 6000 examples, terr=0.0753 fscore=0.9247 train: 6100 examples, terr=0.0751 fscore=0.9249 train: 6200 examples, terr=0.0751 fscore=0.9249 train: 6300 examples, terr=0.0749 fscore=0.9251 train: 6400 examples, terr=0.0754 fscore=0.9246 train: 6500 examples, terr=0.0753 fscore=0.9247 train: 6600 examples, terr=0.0757 fscore=0.9243 train: 6700 examples, terr=0.0755 fscore=0.9245 train: 6800 examples, terr=0.0759 fscore=0.9241 train: 6900 examples, terr=0.0761 fscore=0.9239 train: 7000 examples, terr=0.0757 fscore=0.9243 train: 7100 examples, terr=0.0755 fscore=0.9245 train: 7200 examples, terr=0.0753 fscore=0.9247 train: 7300 examples, terr=0.0755 fscore=0.9245 train: 7400 examples, terr=0.0755 fscore=0.9245 train: 7500 examples, terr=0.0756 fscore=0.9244 train: 7600 examples, terr=0.0755 fscore=0.9245 train: 7700 examples, terr=0.0759 fscore=0.9241 train: 7800 examples, terr=0.0755 fscore=0.9245 train: 7900 examples, terr=0.0755 fscore=0.9245 train: 8000 examples, terr=0.0757 fscore=0.9243 train: 8100 examples, terr=0.0757 fscore=0.9243 train: 8200 examples, terr=0.0761 fscore=0.9239 train: 8300 examples, terr=0.0763 fscore=0.9237 train: 8323 examples, terr=0.0762 fscore=0.9238 iteration 4 train: 100 examples, terr=0.1238 fscore=0.8762 train: 200 examples, terr=0.0660 fscore=0.9340 train: 300 examples, terr=0.0742 fscore=0.9258 train: 400 examples, terr=0.0715 fscore=0.9285 train: 500 examples, terr=0.0685 fscore=0.9315 train: 600 examples, terr=0.0685 fscore=0.9315 train: 700 examples, terr=0.0711 fscore=0.9289 train: 800 examples, terr=0.0706 fscore=0.9294 train: 900 examples, terr=0.0693 fscore=0.9307 train: 1000 examples, terr=0.0690 fscore=0.9310 train: 1100 examples, terr=0.0697 fscore=0.9303 train: 1200 examples, terr=0.0667 fscore=0.9333 train: 1300 examples, terr=0.0649 fscore=0.9351 train: 1400 examples, terr=0.0650 fscore=0.9350 train: 1500 examples, terr=0.0667 fscore=0.9333 train: 1600 examples, terr=0.0650 fscore=0.9350 train: 1700 examples, terr=0.0654 fscore=0.9346 train: 1800 examples, terr=0.0650 fscore=0.9350 train: 1900 examples, terr=0.0655 fscore=0.9345 train: 2000 examples, terr=0.0656 fscore=0.9344 train: 2100 examples, terr=0.0647 fscore=0.9353 train: 2200 examples, terr=0.0628 fscore=0.9372 train: 2300 examples, terr=0.0617 fscore=0.9383 train: 2400 examples, terr=0.0616 fscore=0.9384 train: 2500 examples, terr=0.0628 fscore=0.9372 train: 2600 examples, terr=0.0632 fscore=0.9368 train: 2700 examples, terr=0.0621 fscore=0.9379 train: 2800 examples, terr=0.0625 fscore=0.9375 train: 2900 examples, terr=0.0646 fscore=0.9354 train: 3000 examples, terr=0.0646 fscore=0.9354 train: 3100 examples, terr=0.0647 fscore=0.9353 train: 3200 examples, terr=0.0651 fscore=0.9349 train: 3300 examples, terr=0.0660 fscore=0.9340 train: 3400 examples, terr=0.0651 fscore=0.9349 train: 3500 examples, terr=0.0644 fscore=0.9356 train: 3600 examples, terr=0.0640 fscore=0.9360 train: 3700 examples, terr=0.0644 fscore=0.9356 train: 3800 examples, terr=0.0647 fscore=0.9353 train: 3900 examples, terr=0.0649 fscore=0.9351 train: 4000 examples, terr=0.0649 fscore=0.9351 train: 4100 examples, terr=0.0658 fscore=0.9342 train: 4200 examples, terr=0.0665 fscore=0.9335 train: 4300 examples, terr=0.0668 fscore=0.9332 train: 4400 examples, terr=0.0666 fscore=0.9334 train: 4500 examples, terr=0.0671 fscore=0.9329 train: 4600 examples, terr=0.0675 fscore=0.9325 train: 4700 examples, terr=0.0683 fscore=0.9317 train: 4800 examples, terr=0.0686 fscore=0.9314 train: 4900 examples, terr=0.0693 fscore=0.9307 train: 5000 examples, terr=0.0691 fscore=0.9309 train: 5100 examples, terr=0.0685 fscore=0.9315 train: 5200 examples, terr=0.0686 fscore=0.9314 train: 5300 examples, terr=0.0686 fscore=0.9314 train: 5400 examples, terr=0.0684 fscore=0.9316 train: 5500 examples, terr=0.0686 fscore=0.9314 train: 5600 examples, terr=0.0681 fscore=0.9319 train: 5700 examples, terr=0.0691 fscore=0.9309 train: 5800 examples, terr=0.0689 fscore=0.9311 train: 5900 examples, terr=0.0687 fscore=0.9313 train: 6000 examples, terr=0.0689 fscore=0.9311 train: 6100 examples, terr=0.0689 fscore=0.9311 train: 6200 examples, terr=0.0687 fscore=0.9313 train: 6300 examples, terr=0.0688 fscore=0.9312 train: 6400 examples, terr=0.0689 fscore=0.9311 train: 6500 examples, terr=0.0689 fscore=0.9311 train: 6600 examples, terr=0.0697 fscore=0.9303 train: 6700 examples, terr=0.0699 fscore=0.9301 train: 6800 examples, terr=0.0703 fscore=0.9297 train: 6900 examples, terr=0.0707 fscore=0.9293 train: 7000 examples, terr=0.0704 fscore=0.9296 train: 7100 examples, terr=0.0703 fscore=0.9297 train: 7200 examples, terr=0.0702 fscore=0.9298 train: 7300 examples, terr=0.0703 fscore=0.9297 train: 7400 examples, terr=0.0704 fscore=0.9296 train: 7500 examples, terr=0.0707 fscore=0.9293 train: 7600 examples, terr=0.0706 fscore=0.9294 train: 7700 examples, terr=0.0709 fscore=0.9291 train: 7800 examples, terr=0.0707 fscore=0.9293 train: 7900 examples, terr=0.0705 fscore=0.9295 train: 8000 examples, terr=0.0706 fscore=0.9294 train: 8100 examples, terr=0.0707 fscore=0.9293 train: 8200 examples, terr=0.0709 fscore=0.9291 train: 8300 examples, terr=0.0713 fscore=0.9287 train: 8323 examples, terr=0.0712 fscore=0.9288 iteration 5 train: 100 examples, terr=0.1005 fscore=0.8995 train: 200 examples, terr=0.0599 fscore=0.9401 train: 300 examples, terr=0.0643 fscore=0.9357 train: 400 examples, terr=0.0653 fscore=0.9347 train: 500 examples, terr=0.0655 fscore=0.9345 train: 600 examples, terr=0.0658 fscore=0.9342 train: 700 examples, terr=0.0681 fscore=0.9319 train: 800 examples, terr=0.0666 fscore=0.9334 train: 900 examples, terr=0.0650 fscore=0.9350 train: 1000 examples, terr=0.0646 fscore=0.9354 train: 1100 examples, terr=0.0653 fscore=0.9347 train: 1200 examples, terr=0.0622 fscore=0.9378 train: 1300 examples, terr=0.0617 fscore=0.9383 train: 1400 examples, terr=0.0629 fscore=0.9371 train: 1500 examples, terr=0.0628 fscore=0.9372 train: 1600 examples, terr=0.0612 fscore=0.9388 train: 1700 examples, terr=0.0610 fscore=0.9390 train: 1800 examples, terr=0.0607 fscore=0.9393 train: 1900 examples, terr=0.0612 fscore=0.9388 train: 2000 examples, terr=0.0610 fscore=0.9390 train: 2100 examples, terr=0.0608 fscore=0.9392 train: 2200 examples, terr=0.0591 fscore=0.9409 train: 2300 examples, terr=0.0579 fscore=0.9421 train: 2400 examples, terr=0.0576 fscore=0.9424 train: 2500 examples, terr=0.0582 fscore=0.9418 train: 2600 examples, terr=0.0586 fscore=0.9414 train: 2700 examples, terr=0.0581 fscore=0.9419 train: 2800 examples, terr=0.0577 fscore=0.9423 train: 2900 examples, terr=0.0591 fscore=0.9409 train: 3000 examples, terr=0.0587 fscore=0.9413 train: 3100 examples, terr=0.0588 fscore=0.9412 train: 3200 examples, terr=0.0592 fscore=0.9408 train: 3300 examples, terr=0.0602 fscore=0.9398 train: 3400 examples, terr=0.0599 fscore=0.9401 train: 3500 examples, terr=0.0594 fscore=0.9406 train: 3600 examples, terr=0.0590 fscore=0.9410 train: 3700 examples, terr=0.0592 fscore=0.9408 train: 3800 examples, terr=0.0597 fscore=0.9403 train: 3900 examples, terr=0.0601 fscore=0.9399 train: 4000 examples, terr=0.0601 fscore=0.9399 train: 4100 examples, terr=0.0608 fscore=0.9392 train: 4200 examples, terr=0.0687 fscore=0.9313 train: 4300 examples, terr=0.0682 fscore=0.9318 train: 4400 examples, terr=0.0682 fscore=0.9318 train: 4500 examples, terr=0.0692 fscore=0.9308 train: 4600 examples, terr=0.0696 fscore=0.9304 train: 4700 examples, terr=0.0697 fscore=0.9303 train: 4800 examples, terr=0.0700 fscore=0.9300 train: 4900 examples, terr=0.0705 fscore=0.9295 train: 5000 examples, terr=0.0703 fscore=0.9297 train: 5100 examples, terr=0.0695 fscore=0.9305 train: 5200 examples, terr=0.0692 fscore=0.9308 train: 5300 examples, terr=0.0693 fscore=0.9307 train: 5400 examples, terr=0.0688 fscore=0.9312 train: 5500 examples, terr=0.0694 fscore=0.9306 train: 5600 examples, terr=0.0691 fscore=0.9309 train: 5700 examples, terr=0.0697 fscore=0.9303 train: 5800 examples, terr=0.0695 fscore=0.9305 train: 5900 examples, terr=0.0689 fscore=0.9311 train: 6000 examples, terr=0.0687 fscore=0.9313 train: 6100 examples, terr=0.0688 fscore=0.9312 train: 6200 examples, terr=0.0687 fscore=0.9313 train: 6300 examples, terr=0.0686 fscore=0.9314 train: 6400 examples, terr=0.0689 fscore=0.9311 train: 6500 examples, terr=0.0688 fscore=0.9312 train: 6600 examples, terr=0.0693 fscore=0.9307 train: 6700 examples, terr=0.0693 fscore=0.9307 train: 6800 examples, terr=0.0702 fscore=0.9298 train: 6900 examples, terr=0.0705 fscore=0.9295 train: 7000 examples, terr=0.0703 fscore=0.9297 train: 7100 examples, terr=0.0700 fscore=0.9300 train: 7200 examples, terr=0.0698 fscore=0.9302 train: 7300 examples, terr=0.0701 fscore=0.9299 train: 7400 examples, terr=0.0700 fscore=0.9300 train: 7500 examples, terr=0.0701 fscore=0.9299 train: 7600 examples, terr=0.0698 fscore=0.9302 train: 7700 examples, terr=0.0703 fscore=0.9297 train: 7800 examples, terr=0.0701 fscore=0.9299 train: 7900 examples, terr=0.0701 fscore=0.9299 train: 8000 examples, terr=0.0702 fscore=0.9298 train: 8100 examples, terr=0.0704 fscore=0.9296 train: 8200 examples, terr=0.0706 fscore=0.9294 train: 8300 examples, terr=0.0708 fscore=0.9292 train: 8323 examples, terr=0.0707 fscore=0.9293 iteration 6 train: 100 examples, terr=0.0854 fscore=0.9146 train: 200 examples, terr=0.0543 fscore=0.9457 train: 300 examples, terr=0.0576 fscore=0.9424 train: 400 examples, terr=0.0596 fscore=0.9404 train: 500 examples, terr=0.0579 fscore=0.9421 train: 600 examples, terr=0.0596 fscore=0.9404 train: 700 examples, terr=0.0627 fscore=0.9373 train: 800 examples, terr=0.0598 fscore=0.9402 train: 900 examples, terr=0.0576 fscore=0.9424 train: 1000 examples, terr=0.0574 fscore=0.9426 train: 1100 examples, terr=0.0579 fscore=0.9421 train: 1200 examples, terr=0.0557 fscore=0.9443 train: 1300 examples, terr=0.0551 fscore=0.9449 train: 1400 examples, terr=0.0553 fscore=0.9447 train: 1500 examples, terr=0.0558 fscore=0.9442 train: 1600 examples, terr=0.0552 fscore=0.9448 train: 1700 examples, terr=0.0548 fscore=0.9452 train: 1800 examples, terr=0.0543 fscore=0.9457 train: 1900 examples, terr=0.0549 fscore=0.9451 train: 2000 examples, terr=0.0549 fscore=0.9451 train: 2100 examples, terr=0.0543 fscore=0.9457 train: 2200 examples, terr=0.0536 fscore=0.9464 train: 2300 examples, terr=0.0528 fscore=0.9472 train: 2400 examples, terr=0.0535 fscore=0.9465 train: 2500 examples, terr=0.0545 fscore=0.9455 train: 2600 examples, terr=0.0553 fscore=0.9447 train: 2700 examples, terr=0.0545 fscore=0.9455 train: 2800 examples, terr=0.0547 fscore=0.9453 train: 2900 examples, terr=0.0562 fscore=0.9438 train: 3000 examples, terr=0.0557 fscore=0.9443 train: 3100 examples, terr=0.0555 fscore=0.9445 train: 3200 examples, terr=0.0560 fscore=0.9440 train: 3300 examples, terr=0.0574 fscore=0.9426 train: 3400 examples, terr=0.0569 fscore=0.9431 train: 3500 examples, terr=0.0564 fscore=0.9436 train: 3600 examples, terr=0.0564 fscore=0.9436 train: 3700 examples, terr=0.0565 fscore=0.9435 train: 3800 examples, terr=0.0567 fscore=0.9433 train: 3900 examples, terr=0.0569 fscore=0.9431 train: 4000 examples, terr=0.0571 fscore=0.9429 train: 4100 examples, terr=0.0573 fscore=0.9427 train: 4200 examples, terr=0.0583 fscore=0.9417 train: 4300 examples, terr=0.0588 fscore=0.9412 train: 4400 examples, terr=0.0592 fscore=0.9408 train: 4500 examples, terr=0.0613 fscore=0.9387 train: 4600 examples, terr=0.0614 fscore=0.9386 train: 4700 examples, terr=0.0622 fscore=0.9378 train: 4800 examples, terr=0.0624 fscore=0.9376 train: 4900 examples, terr=0.0628 fscore=0.9372 train: 5000 examples, terr=0.0626 fscore=0.9374 train: 5100 examples, terr=0.0622 fscore=0.9378 train: 5200 examples, terr=0.0618 fscore=0.9382 train: 5300 examples, terr=0.0617 fscore=0.9383 train: 5400 examples, terr=0.0612 fscore=0.9388 train: 5500 examples, terr=0.0615 fscore=0.9385 train: 5600 examples, terr=0.0613 fscore=0.9387 train: 5700 examples, terr=0.0617 fscore=0.9383 train: 5800 examples, terr=0.0618 fscore=0.9382 train: 5900 examples, terr=0.0613 fscore=0.9387 train: 6000 examples, terr=0.0613 fscore=0.9387 train: 6100 examples, terr=0.0612 fscore=0.9388 train: 6200 examples, terr=0.0612 fscore=0.9388 train: 6300 examples, terr=0.0611 fscore=0.9389 train: 6400 examples, terr=0.0613 fscore=0.9387 train: 6500 examples, terr=0.0613 fscore=0.9387 train: 6600 examples, terr=0.0616 fscore=0.9384 train: 6700 examples, terr=0.0615 fscore=0.9385 train: 6800 examples, terr=0.0623 fscore=0.9377 train: 6900 examples, terr=0.0627 fscore=0.9373 train: 7000 examples, terr=0.0625 fscore=0.9375 train: 7100 examples, terr=0.0621 fscore=0.9379 train: 7200 examples, terr=0.0621 fscore=0.9379 train: 7300 examples, terr=0.0621 fscore=0.9379 train: 7400 examples, terr=0.0622 fscore=0.9378 train: 7500 examples, terr=0.0624 fscore=0.9376 train: 7600 examples, terr=0.0624 fscore=0.9376 train: 7700 examples, terr=0.0628 fscore=0.9372 train: 7800 examples, terr=0.0626 fscore=0.9374 train: 7900 examples, terr=0.0626 fscore=0.9374 train: 8000 examples, terr=0.0628 fscore=0.9372 train: 8100 examples, terr=0.0629 fscore=0.9371 train: 8200 examples, terr=0.0632 fscore=0.9368 train: 8300 examples, terr=0.0635 fscore=0.9365 train: 8323 examples, terr=0.0634 fscore=0.9366 iteration 7 train: 100 examples, terr=0.0813 fscore=0.9187 train: 200 examples, terr=0.0519 fscore=0.9481 train: 300 examples, terr=0.0569 fscore=0.9431 train: 400 examples, terr=0.0545 fscore=0.9455 train: 500 examples, terr=0.0555 fscore=0.9445 train: 600 examples, terr=0.0595 fscore=0.9405 train: 700 examples, terr=0.0638 fscore=0.9362 train: 800 examples, terr=0.0624 fscore=0.9376 train: 900 examples, terr=0.0593 fscore=0.9407 train: 1000 examples, terr=0.0615 fscore=0.9385 train: 1100 examples, terr=0.0617 fscore=0.9383 train: 1200 examples, terr=0.0585 fscore=0.9415 train: 1300 examples, terr=0.0573 fscore=0.9427 train: 1400 examples, terr=0.0575 fscore=0.9425 train: 1500 examples, terr=0.0590 fscore=0.9410 train: 1600 examples, terr=0.0584 fscore=0.9416 train: 1700 examples, terr=0.0583 fscore=0.9417 train: 1800 examples, terr=0.0579 fscore=0.9421 train: 1900 examples, terr=0.0584 fscore=0.9416 train: 2000 examples, terr=0.0583 fscore=0.9417 train: 2100 examples, terr=0.0574 fscore=0.9426 train: 2200 examples, terr=0.0561 fscore=0.9439 train: 2300 examples, terr=0.0552 fscore=0.9448 train: 2400 examples, terr=0.0552 fscore=0.9448 train: 2500 examples, terr=0.0562 fscore=0.9438 train: 2600 examples, terr=0.0565 fscore=0.9435 train: 2700 examples, terr=0.0557 fscore=0.9443 train: 2800 examples, terr=0.0558 fscore=0.9442 train: 2900 examples, terr=0.0568 fscore=0.9432 train: 3000 examples, terr=0.0562 fscore=0.9438 train: 3100 examples, terr=0.0561 fscore=0.9439 train: 3200 examples, terr=0.0560 fscore=0.9440 train: 3300 examples, terr=0.0569 fscore=0.9431 train: 3400 examples, terr=0.0563 fscore=0.9437 train: 3500 examples, terr=0.0559 fscore=0.9441 train: 3600 examples, terr=0.0557 fscore=0.9443 train: 3700 examples, terr=0.0553 fscore=0.9447 train: 3800 examples, terr=0.0555 fscore=0.9445 train: 3900 examples, terr=0.0557 fscore=0.9443 train: 4000 examples, terr=0.0560 fscore=0.9440 train: 4100 examples, terr=0.0566 fscore=0.9434 train: 4200 examples, terr=0.0575 fscore=0.9425 train: 4300 examples, terr=0.0578 fscore=0.9422 train: 4400 examples, terr=0.0578 fscore=0.9422 train: 4500 examples, terr=0.0599 fscore=0.9401 train: 4600 examples, terr=0.0598 fscore=0.9402 train: 4700 examples, terr=0.0601 fscore=0.9399 train: 4800 examples, terr=0.0603 fscore=0.9397 train: 4900 examples, terr=0.0610 fscore=0.9390 train: 5000 examples, terr=0.0609 fscore=0.9391 train: 5100 examples, terr=0.0603 fscore=0.9397 train: 5200 examples, terr=0.0601 fscore=0.9399 train: 5300 examples, terr=0.0602 fscore=0.9398 train: 5400 examples, terr=0.0601 fscore=0.9399 train: 5500 examples, terr=0.0605 fscore=0.9395 train: 5600 examples, terr=0.0602 fscore=0.9398 train: 5700 examples, terr=0.0609 fscore=0.9391 train: 5800 examples, terr=0.0607 fscore=0.9393 train: 5900 examples, terr=0.0602 fscore=0.9398 train: 6000 examples, terr=0.0604 fscore=0.9396 train: 6100 examples, terr=0.0604 fscore=0.9396 train: 6200 examples, terr=0.0602 fscore=0.9398 train: 6300 examples, terr=0.0602 fscore=0.9398 train: 6400 examples, terr=0.0605 fscore=0.9395 train: 6500 examples, terr=0.0605 fscore=0.9395 train: 6600 examples, terr=0.0607 fscore=0.9393 train: 6700 examples, terr=0.0607 fscore=0.9393 train: 6800 examples, terr=0.0614 fscore=0.9386 train: 6900 examples, terr=0.0617 fscore=0.9383 train: 7000 examples, terr=0.0618 fscore=0.9382 train: 7100 examples, terr=0.0617 fscore=0.9383 train: 7200 examples, terr=0.0616 fscore=0.9384 train: 7300 examples, terr=0.0620 fscore=0.9380 train: 7400 examples, terr=0.0619 fscore=0.9381 train: 7500 examples, terr=0.0620 fscore=0.9380 train: 7600 examples, terr=0.0621 fscore=0.9379 train: 7700 examples, terr=0.0625 fscore=0.9375 train: 7800 examples, terr=0.0623 fscore=0.9377 train: 7900 examples, terr=0.0623 fscore=0.9377 train: 8000 examples, terr=0.0624 fscore=0.9376 train: 8100 examples, terr=0.0624 fscore=0.9376 train: 8200 examples, terr=0.0627 fscore=0.9373 train: 8300 examples, terr=0.0628 fscore=0.9372 train: 8323 examples, terr=0.0627 fscore=0.9373 iteration 8 train: 100 examples, terr=0.0746 fscore=0.9254 train: 200 examples, terr=0.0478 fscore=0.9522 train: 300 examples, terr=0.0493 fscore=0.9507 train: 400 examples, terr=0.0519 fscore=0.9481 train: 500 examples, terr=0.0526 fscore=0.9474 train: 600 examples, terr=0.0545 fscore=0.9455 train: 700 examples, terr=0.0580 fscore=0.9420 train: 800 examples, terr=0.0575 fscore=0.9425 train: 900 examples, terr=0.0560 fscore=0.9440 train: 1000 examples, terr=0.0567 fscore=0.9433 train: 1100 examples, terr=0.0570 fscore=0.9430 train: 1200 examples, terr=0.0549 fscore=0.9451 train: 1300 examples, terr=0.0537 fscore=0.9463 train: 1400 examples, terr=0.0545 fscore=0.9455 train: 1500 examples, terr=0.0548 fscore=0.9452 train: 1600 examples, terr=0.0538 fscore=0.9462 train: 1700 examples, terr=0.0537 fscore=0.9463 train: 1800 examples, terr=0.0535 fscore=0.9465 train: 1900 examples, terr=0.0539 fscore=0.9461 train: 2000 examples, terr=0.0538 fscore=0.9462 train: 2100 examples, terr=0.0531 fscore=0.9469 train: 2200 examples, terr=0.0518 fscore=0.9482 train: 2300 examples, terr=0.0510 fscore=0.9490 train: 2400 examples, terr=0.0515 fscore=0.9485 train: 2500 examples, terr=0.0522 fscore=0.9478 train: 2600 examples, terr=0.0525 fscore=0.9475 train: 2700 examples, terr=0.0517 fscore=0.9483 train: 2800 examples, terr=0.0517 fscore=0.9483 train: 2900 examples, terr=0.0533 fscore=0.9467 train: 3000 examples, terr=0.0527 fscore=0.9473 train: 3100 examples, terr=0.0527 fscore=0.9473 train: 3200 examples, terr=0.0529 fscore=0.9471 train: 3300 examples, terr=0.0539 fscore=0.9461 train: 3400 examples, terr=0.0529 fscore=0.9471 train: 3500 examples, terr=0.0526 fscore=0.9474 train: 3600 examples, terr=0.0524 fscore=0.9476 train: 3700 examples, terr=0.0524 fscore=0.9476 train: 3800 examples, terr=0.0527 fscore=0.9473 train: 3900 examples, terr=0.0531 fscore=0.9469 train: 4000 examples, terr=0.0532 fscore=0.9468 train: 4100 examples, terr=0.0542 fscore=0.9458 train: 4200 examples, terr=0.0553 fscore=0.9447 train: 4300 examples, terr=0.0558 fscore=0.9442 train: 4400 examples, terr=0.0558 fscore=0.9442 train: 4500 examples, terr=0.0575 fscore=0.9425 train: 4600 examples, terr=0.0577 fscore=0.9423 train: 4700 examples, terr=0.0579 fscore=0.9421 train: 4800 examples, terr=0.0581 fscore=0.9419 train: 4900 examples, terr=0.0587 fscore=0.9413 train: 5000 examples, terr=0.0587 fscore=0.9413 train: 5100 examples, terr=0.0582 fscore=0.9418 train: 5200 examples, terr=0.0580 fscore=0.9420 train: 5300 examples, terr=0.0580 fscore=0.9420 train: 5400 examples, terr=0.0577 fscore=0.9423 train: 5500 examples, terr=0.0582 fscore=0.9418 train: 5600 examples, terr=0.0577 fscore=0.9423 train: 5700 examples, terr=0.0581 fscore=0.9419 train: 5800 examples, terr=0.0579 fscore=0.9421 train: 5900 examples, terr=0.0574 fscore=0.9426 train: 6000 examples, terr=0.0576 fscore=0.9424 train: 6100 examples, terr=0.0575 fscore=0.9425 train: 6200 examples, terr=0.0575 fscore=0.9425 train: 6300 examples, terr=0.0574 fscore=0.9426 train: 6400 examples, terr=0.0573 fscore=0.9427 train: 6500 examples, terr=0.0574 fscore=0.9426 train: 6600 examples, terr=0.0578 fscore=0.9422 train: 6700 examples, terr=0.0579 fscore=0.9421 train: 6800 examples, terr=0.0585 fscore=0.9415 train: 6900 examples, terr=0.0587 fscore=0.9413 train: 7000 examples, terr=0.0586 fscore=0.9414 train: 7100 examples, terr=0.0584 fscore=0.9416 train: 7200 examples, terr=0.0582 fscore=0.9418 train: 7300 examples, terr=0.0583 fscore=0.9417 train: 7400 examples, terr=0.0581 fscore=0.9419 train: 7500 examples, terr=0.0583 fscore=0.9417 train: 7600 examples, terr=0.0584 fscore=0.9416 train: 7700 examples, terr=0.0586 fscore=0.9414 train: 7800 examples, terr=0.0584 fscore=0.9416 train: 7900 examples, terr=0.0586 fscore=0.9414 train: 8000 examples, terr=0.0585 fscore=0.9415 train: 8100 examples, terr=0.0588 fscore=0.9412 train: 8200 examples, terr=0.0592 fscore=0.9408 train: 8300 examples, terr=0.0594 fscore=0.9406 train: 8323 examples, terr=0.0593 fscore=0.9407 iteration 9 train: 100 examples, terr=0.0798 fscore=0.9202 train: 200 examples, terr=0.0496 fscore=0.9504 train: 300 examples, terr=0.0516 fscore=0.9484 train: 400 examples, terr=0.0501 fscore=0.9499 train: 500 examples, terr=0.0466 fscore=0.9534 train: 600 examples, terr=0.0480 fscore=0.9520 train: 700 examples, terr=0.0519 fscore=0.9481 train: 800 examples, terr=0.0516 fscore=0.9484 train: 900 examples, terr=0.0514 fscore=0.9486 train: 1000 examples, terr=0.0516 fscore=0.9484 train: 1100 examples, terr=0.0522 fscore=0.9478 train: 1200 examples, terr=0.0503 fscore=0.9497 train: 1300 examples, terr=0.0495 fscore=0.9505 train: 1400 examples, terr=0.0495 fscore=0.9505 train: 1500 examples, terr=0.0500 fscore=0.9500 train: 1600 examples, terr=0.0492 fscore=0.9508 train: 1700 examples, terr=0.0495 fscore=0.9505 train: 1800 examples, terr=0.0496 fscore=0.9504 train: 1900 examples, terr=0.0506 fscore=0.9494 train: 2000 examples, terr=0.0505 fscore=0.9495 train: 2100 examples, terr=0.0497 fscore=0.9503 train: 2200 examples, terr=0.0484 fscore=0.9516 train: 2300 examples, terr=0.0479 fscore=0.9521 train: 2400 examples, terr=0.0482 fscore=0.9518 train: 2500 examples, terr=0.0493 fscore=0.9507 train: 2600 examples, terr=0.0499 fscore=0.9501 train: 2700 examples, terr=0.0491 fscore=0.9509 train: 2800 examples, terr=0.0496 fscore=0.9504 train: 2900 examples, terr=0.0507 fscore=0.9493 train: 3000 examples, terr=0.0502 fscore=0.9498 train: 3100 examples, terr=0.0509 fscore=0.9491 train: 3200 examples, terr=0.0509 fscore=0.9491 train: 3300 examples, terr=0.0514 fscore=0.9486 train: 3400 examples, terr=0.0511 fscore=0.9489 train: 3500 examples, terr=0.0507 fscore=0.9493 train: 3600 examples, terr=0.0506 fscore=0.9494 train: 3700 examples, terr=0.0508 fscore=0.9492 train: 3800 examples, terr=0.0513 fscore=0.9487 train: 3900 examples, terr=0.0516 fscore=0.9484 train: 4000 examples, terr=0.0519 fscore=0.9481 train: 4100 examples, terr=0.0521 fscore=0.9479 train: 4200 examples, terr=0.0532 fscore=0.9468 train: 4300 examples, terr=0.0532 fscore=0.9468 train: 4400 examples, terr=0.0533 fscore=0.9467 train: 4500 examples, terr=0.0542 fscore=0.9458 train: 4600 examples, terr=0.0543 fscore=0.9457 train: 4700 examples, terr=0.0547 fscore=0.9453 train: 4800 examples, terr=0.0549 fscore=0.9451 train: 4900 examples, terr=0.0554 fscore=0.9446 train: 5000 examples, terr=0.0552 fscore=0.9448 train: 5100 examples, terr=0.0546 fscore=0.9454 train: 5200 examples, terr=0.0546 fscore=0.9454 train: 5300 examples, terr=0.0546 fscore=0.9454 train: 5400 examples, terr=0.0544 fscore=0.9456 train: 5500 examples, terr=0.0548 fscore=0.9452 train: 5600 examples, terr=0.0548 fscore=0.9452 train: 5700 examples, terr=0.0556 fscore=0.9444 train: 5800 examples, terr=0.0556 fscore=0.9444 train: 5900 examples, terr=0.0557 fscore=0.9443 train: 6000 examples, terr=0.0557 fscore=0.9443 train: 6100 examples, terr=0.0558 fscore=0.9442 train: 6200 examples, terr=0.0557 fscore=0.9443 train: 6300 examples, terr=0.0556 fscore=0.9444 train: 6400 examples, terr=0.0558 fscore=0.9442 train: 6500 examples, terr=0.0558 fscore=0.9442 train: 6600 examples, terr=0.0563 fscore=0.9437 train: 6700 examples, terr=0.0563 fscore=0.9437 train: 6800 examples, terr=0.0565 fscore=0.9435 train: 6900 examples, terr=0.0569 fscore=0.9431 train: 7000 examples, terr=0.0568 fscore=0.9432 train: 7100 examples, terr=0.0565 fscore=0.9435 train: 7200 examples, terr=0.0564 fscore=0.9436 train: 7300 examples, terr=0.0565 fscore=0.9435 train: 7400 examples, terr=0.0565 fscore=0.9435 train: 7500 examples, terr=0.0565 fscore=0.9435 train: 7600 examples, terr=0.0564 fscore=0.9436 train: 7700 examples, terr=0.0567 fscore=0.9433 train: 7800 examples, terr=0.0566 fscore=0.9434 train: 7900 examples, terr=0.0565 fscore=0.9435 train: 8000 examples, terr=0.0568 fscore=0.9432 train: 8100 examples, terr=0.0569 fscore=0.9431 train: 8200 examples, terr=0.0571 fscore=0.9429 train: 8300 examples, terr=0.0574 fscore=0.9426 train: 8323 examples, terr=0.0573 fscore=0.9427 writing model: model === END program1: ./run learn ../dataset2/train --- OK [18s] ===== 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 [1s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out reading model: model === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [6s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [8s] ===== 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 reading model: model === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s] real 0m36.601s user 0m30.418s sys 0m3.872s
Run specification 
Results
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