#for logistic regression method, we tried both the first order and second order #terms (linear or quadratic), and which are indicated by suffices "a"(additive) #or "m"(multicative). For example, glm.0111.a uses the linear from, and #glm.0111.m uses the quadratic form. TP FP FN sensitivity specificity knn.0001 665 938 1479 0.310167910447761 0.414847 knn.0010 661 942 1483 0.30830223880597 0.412352 knn.0011 880 723 1264 0.41044776119403 0.548971 knn.0100 751 852 1393 0.350279850746269 0.468497 knn.0101 757 846 1387 0.353078358208955 0.47224 knn.0110 803 800 1341 0.374533582089552 0.500936 knn.0111 806 797 1338 0.375932835820896 0.502807 knn.1000 636 967 1508 0.296641791044776 0.396756 knn.1001 956 647 1188 0.44589552238806 0.596382 knn.1010 843 760 1301 0.393190298507463 0.525889 knn.1011 979 624 1165 0.456623134328358 0.61073 knn.1100 815 788 1329 0.380130597014925 0.508422 knn.1101 820 783 1324 0.382462686567164 0.511541 knn.1110 827 776 1317 0.385727611940299 0.515908 knn.1111 831 772 1313 0.387593283582090 0.518403 glm.0001 913 690 1231 0.425839552238806 0.569557 glm.0010 866 737 1278 0.403917910447761 0.540237 glm.0100 759 844 1385 0.354011194029851 0.473487 glm.0111.a 1017 586 1127 0.474347014925373 0.634435 glm.0111.m 1056 547 1088 0.492537313432836 0.658764 glm.1000 702 901 1442 0.327425373134328 0.437928 glm.1010.a 809 794 1335 0.377332089552239 0.504678 glm.1010.m 809 794 1335 0.377332089552239 0.504678 glm.1011.a 1094 509 1050 0.510261194029851 0.682470 glm.1011.m 1099 504 1045 0.51259328358209 0.685589 glm.1100.a 704 899 1440 0.328358208955224 0.439176 glm.1100.m 992 611 1152 0.462686567164179 0.618839 glm.1101.a 1063 540 1081 0.49580223880597 0.663131 glm.1101.m 1073 530 1071 0.500466417910448 0.669369 glm.1110.a 873 730 1271 0.407182835820896 0.544603 glm.1110.m 1037 566 1107 0.483675373134328 0.646912 glm.1111.a 1038 565 1106 0.484141791044776 0.647535 glm.1111.m 1088 515 1056 0.507462686567164 0.678727 loess.0100 895 708 1249 0.417444029850746 0.558328 loess.0111 1055 548 1089 0.492070895522388 0.658140 loess.1000 827 776 1317 0.385727611940299 0.515907 loess.1010 890 713 1254 0.415111940298507 0.555208 loess.1011 1098 505 1046 0.512126865671642 0.684965 loess.1100 1011 592 1133 0.471548507462687 0.630692 loess.1101 1071 532 1073 0.499533582089552 0.668122 loess.1110 1048 555 1096 0.488805970149254 0.653774 loess.1111 1074 529 1070 0.500932835820896 0.669993