Status: Failed!
Note: this page autoupdates while a run is in progress
Reason
(see end of log file)
Total Time
5h0m
Max Memory Usage
1197M
Domain
BinaryClassification
Learn time
Train error
Predict train time
Test error
Predict test time
Log file
... (lines omitted) ...
rnd 174: wh-err= 0.999927 th-err= 0.966399 test= -nan train= 0.3999872
rnd 175: wh-err= 0.999935 th-err= 0.966336 test= -nan train= 0.4000222
rnd 176: wh-err= 0.999938 th-err= 0.966276 test= -nan train= 0.3997665
rnd 177: wh-err= 0.999938 th-err= 0.966216 test= -nan train= 0.3996143
rnd 178: wh-err= 0.999938 th-err= 0.966156 test= -nan train= 0.3996143
rnd 179: wh-err= 0.999939 th-err= 0.966097 test= -nan train= 0.3995207
rnd 180: wh-err= 0.999939 th-err= 0.966038 test= -nan train= 0.3994261
rnd 181: wh-err= 0.999939 th-err= 0.965978 test= -nan train= 0.3993217
rnd 182: wh-err= 0.999939 th-err= 0.965919 test= -nan train= 0.3991744
rnd 183: wh-err= 0.999939 th-err= 0.965860 test= -nan train= 0.3990374
rnd 184: wh-err= 0.999939 th-err= 0.965800 test= -nan train= 0.3987300
rnd 185: wh-err= 0.999939 th-err= 0.965741 test= -nan train= 0.3987163
rnd 186: wh-err= 0.999939 th-err= 0.965683 test= -nan train= 0.3985995
rnd 187: wh-err= 0.999940 th-err= 0.965625 test= -nan train= 0.3985975
rnd 188: wh-err= 0.999940 th-err= 0.965567 test= -nan train= 0.3985941
rnd 189: wh-err= 0.999940 th-err= 0.965509 test= -nan train= 0.3985936
rnd 190: wh-err= 0.999941 th-err= 0.965452 test= -nan train= 0.3985926
rnd 191: wh-err= 0.999941 th-err= 0.965395 test= -nan train= 0.3984665
rnd 192: wh-err= 0.999941 th-err= 0.965338 test= -nan train= 0.3983690
rnd 193: wh-err= 0.999939 th-err= 0.965279 test= -nan train= 0.3983704
rnd 194: wh-err= 0.999931 th-err= 0.965213 test= -nan train= 0.3982300
rnd 195: wh-err= 0.999941 th-err= 0.965156 test= -nan train= 0.3980729
rnd 196: wh-err= 0.999942 th-err= 0.965100 test= -nan train= 0.3979355
rnd 197: wh-err= 0.999942 th-err= 0.965044 test= -nan train= 0.3979350
rnd 198: wh-err= 0.999943 th-err= 0.964988 test= -nan train= 0.3978212
rnd 199: wh-err= 0.999942 th-err= 0.964933 test= -nan train= 0.3977562
rnd 200: wh-err= 0.999942 th-err= 0.964877 test= -nan train= 0.3977118
rnd 201: wh-err= 0.999942 th-err= 0.964822 test= -nan train= 0.3975823
rnd 202: wh-err= 0.999943 th-err= 0.964766 test= -nan train= 0.3974645
rnd 203: wh-err= 0.999943 th-err= 0.964711 test= -nan train= 0.3973739
rnd 204: wh-err= 0.999943 th-err= 0.964656 test= -nan train= 0.3972453
rnd 205: wh-err= 0.999943 th-err= 0.964601 test= -nan train= 0.3970921
rnd 206: wh-err= 0.999943 th-err= 0.964546 test= -nan train= 0.3970108
rnd 207: wh-err= 0.999943 th-err= 0.964491 test= -nan train= 0.3970113
rnd 208: wh-err= 0.999943 th-err= 0.964437 test= -nan train= 0.3970034
rnd 209: wh-err= 0.999943 th-err= 0.964382 test= -nan train= 0.3969867
rnd 210: wh-err= 0.999944 th-err= 0.964328 test= -nan train= 0.3968946
rnd 211: wh-err= 0.999944 th-err= 0.964273 test= -nan train= 0.3967660
rnd 212: wh-err= 0.999944 th-err= 0.964219 test= -nan train= 0.3967384
rnd 213: wh-err= 0.999944 th-err= 0.964165 test= -nan train= 0.3966148
rnd 214: wh-err= 0.999944 th-err= 0.964111 test= -nan train= 0.3964828
rnd 215: wh-err= 0.999943 th-err= 0.964056 test= -nan train= 0.3966167
rnd 216: wh-err= 0.999936 th-err= 0.963995 test= -nan train= 0.3961961
rnd 217: wh-err= 0.999944 th-err= 0.963941 test= -nan train= 0.3961094
rnd 218: wh-err= 0.999944 th-err= 0.963887 test= -nan train= 0.3962591
rnd 219: wh-err= 0.999944 th-err= 0.963834 test= -nan train= 0.3961384
rnd 220: wh-err= 0.999945 th-err= 0.963780 test= -nan train= 0.3960241
rnd 221: wh-err= 0.999945 th-err= 0.963727 test= -nan train= 0.3959714
rnd 222: wh-err= 0.999945 th-err= 0.963674 test= -nan train= 0.3958936
rnd 223: wh-err= 0.999945 th-err= 0.963620 test= -nan train= 0.3958167
rnd 224: wh-err= 0.999945 th-err= 0.963567 test= -nan train= 0.3956990
rnd 225: wh-err= 0.999945 th-err= 0.963514 test= -nan train= 0.3955857
rnd 226: wh-err= 0.999945 th-err= 0.963462 test= -nan train= 0.3954739
rnd 227: wh-err= 0.999945 th-err= 0.963409 test= -nan train= 0.3953429
rnd 228: wh-err= 0.999945 th-err= 0.963356 test= -nan train= 0.3952783
rnd 229: wh-err= 0.999946 th-err= 0.963304 test= -nan train= 0.3951650
rnd 230: wh-err= 0.999946 th-err= 0.963252 test= -nan train= 0.3950626
rnd 231: wh-err= 0.999946 th-err= 0.963200 test= -nan train= 0.3948542
rnd 232: wh-err= 0.999947 th-err= 0.963149 test= -nan train= 0.3947187
rnd 233: wh-err= 0.999947 th-err= 0.963097 test= -nan train= 0.3947217
rnd 234: wh-err= 0.999947 th-err= 0.963046 test= -nan train= 0.3945985
rnd 235: wh-err= 0.999947 th-err= 0.962995 test= -nan train= 0.3943581
rnd 236: wh-err= 0.999948 th-err= 0.962945 test= -nan train= 0.3942384
rnd 237: wh-err= 0.999948 th-err= 0.962895 test= -nan train= 0.3941394
rnd 238: wh-err= 0.999948 th-err= 0.962844 test= -nan train= 0.3940414
rnd 239: wh-err= 0.999948 th-err= 0.962794 test= -nan train= 0.3940404
rnd 240: wh-err= 0.999948 th-err= 0.962744 test= -nan train= 0.3939404
rnd 241: wh-err= 0.999948 th-err= 0.962694 test= -nan train= 0.3938232
rnd 242: wh-err= 0.999949 th-err= 0.962645 test= -nan train= 0.3937291
rnd 243: wh-err= 0.999949 th-err= 0.962596 test= -nan train= 0.3936601
rnd 244: wh-err= 0.999949 th-err= 0.962547 test= -nan train= 0.3935527
rnd 245: wh-err= 0.999949 th-err= 0.962497 test= -nan train= 0.3934305
rnd 246: wh-err= 0.999949 th-err= 0.962448 test= -nan train= 0.3933182
rnd 247: wh-err= 0.999949 th-err= 0.962399 test= -nan train= 0.3931754
rnd 248: wh-err= 0.999949 th-err= 0.962351 test= -nan train= 0.3930941
rnd 249: wh-err= 0.999949 th-err= 0.962302 test= -nan train= 0.3929852
rnd 250: wh-err= 0.999950 th-err= 0.962254 test= -nan train= 0.3929571
rnd 251: wh-err= 0.999950 th-err= 0.962206 test= -nan train= 0.3929167
rnd 252: wh-err= 0.999950 th-err= 0.962158 test= -nan train= 0.3928310
rnd 253: wh-err= 0.999950 th-err= 0.962110 test= -nan train= 0.3926985
rnd 254: wh-err= 0.999950 th-err= 0.962063 test= -nan train= 0.3926108
rnd 255: wh-err= 0.999951 th-err= 0.962015 test= -nan train= 0.3926069
rnd 256: wh-err= 0.999951 th-err= 0.961968 test= -nan train= 0.3925099
rnd 257: wh-err= 0.999951 th-err= 0.961920 test= -nan train= 0.3925049
rnd 258: wh-err= 0.999951 th-err= 0.961873 test= -nan train= 0.3925049
rnd 259: wh-err= 0.999951 th-err= 0.961826 test= -nan train= 0.3925064
rnd 260: wh-err= 0.999951 th-err= 0.961780 test= -nan train= 0.3923837
rnd 261: wh-err= 0.999951 th-err= 0.961733 test= -nan train= 0.3922138
rnd 262: wh-err= 0.999951 th-err= 0.961686 test= -nan train= 0.3921330
rnd 263: wh-err= 0.999951 th-err= 0.961640 test= -nan train= 0.3920330
rnd 264: wh-err= 0.999952 th-err= 0.961593 test= -nan train= 0.3920192
rnd 265: wh-err= 0.999952 th-err= 0.961547 test= -nan train= 0.3920192
rnd 266: wh-err= 0.999952 th-err= 0.961501 test= -nan train= 0.3918640
rnd 267: wh-err= 0.999952 th-err= 0.961455 test= -nan train= 0.3918483
rnd 268: wh-err= 0.999953 th-err= 0.961409 test= -nan train= 0.3918468
rnd 269: wh-err= 0.999953 th-err= 0.961364 test= -nan train= 0.3918463
rnd 270: wh-err= 0.999953 th-err= 0.961318 test= -nan train= 0.3918700
rnd 271: wh-err= 0.999953 th-err= 0.961273 test= -nan train= 0.3918700
rnd 272: wh-err= 0.999953 th-err= 0.961228 test= -nan train= 0.3917458
rnd 273: wh-err= 0.999953 th-err= 0.961183 test= -nan train= 0.3917443
rnd 274: wh-err= 0.999954 th-err= 0.961138 test= -nan train= 0.3916557
rnd 275: wh-err= 0.999954 th-err= 0.961094 test= -nan train= 0.3914700
rnd 276: wh-err= 0.999954 th-err= 0.961049 test= -nan train= 0.3913768
rnd 277: wh-err= 0.999954 th-err= 0.961005 test= -nan train= 0.3913468
rnd 278: wh-err= 0.999954 th-err= 0.960961 test= -nan train= 0.3912030
rnd 279: wh-err= 0.999954 th-err= 0.960917 test= -nan train= 0.3912025
rnd 280: wh-err= 0.999954 th-err= 0.960873 test= -nan train= 0.3912039
rnd 281: wh-err= 0.999955 th-err= 0.960830 test= -nan train= 0.3911034
rnd 282: wh-err= 0.999955 th-err= 0.960786 test= -nan train= 0.3909562
rnd 283: wh-err= 0.999955 th-err= 0.960743 test= -nan train= 0.3908217
rnd 284: wh-err= 0.999955 th-err= 0.960700 test= -nan train= 0.3907153
rnd 285: wh-err= 0.999955 th-err= 0.960657 test= -nan train= 0.3906246
rnd 286: wh-err= 0.999955 th-err= 0.960614 test= -nan train= 0.3905567
rnd 287: wh-err= 0.999956 th-err= 0.960572 test= -nan train= 0.3905557
rnd 288: wh-err= 0.999956 th-err= 0.960529 test= -nan train= 0.3904532
rnd 289: wh-err= 0.999956 th-err= 0.960487 test= -nan train= 0.3903502
rnd 290: wh-err= 0.999956 th-err= 0.960445 test= -nan train= 0.3902493
rnd 291: wh-err= 0.999956 th-err= 0.960402 test= -nan train= 0.3902488
rnd 292: wh-err= 0.999956 th-err= 0.960360 test= -nan train= 0.3902502
rnd 293: wh-err= 0.999956 th-err= 0.960319 test= -nan train= 0.3901276
rnd 294: wh-err= 0.999957 th-err= 0.960277 test= -nan train= 0.3900778
rnd 295: wh-err= 0.999957 th-err= 0.960235 test= -nan train= 0.3900010
rnd 296: wh-err= 0.999957 th-err= 0.960194 test= -nan train= 0.3898941
rnd 297: wh-err= 0.999957 th-err= 0.960152 test= -nan train= 0.3897404
rnd 298: wh-err= 0.999957 th-err= 0.960111 test= -nan train= 0.3896443
rnd 299: wh-err= 0.999957 th-err= 0.960070 test= -nan train= 0.3895759
rnd 300: wh-err= 0.999957 th-err= 0.960029 test= -nan train= 0.3895645
rnd 301: wh-err= 0.999957 th-err= 0.959987 test= -nan train= 0.3893754
rnd 302: wh-err= 0.999957 th-err= 0.959946 test= -nan train= 0.3892793
rnd 303: wh-err= 0.999946 th-err= 0.959894 test= -nan train= 0.3892921
rnd 304: wh-err= 0.999952 th-err= 0.959848 test= -nan train= 0.3890877
rnd 305: wh-err= 0.999945 th-err= 0.959795 test= -nan train= 0.3890823
rnd 306: wh-err= 0.999956 th-err= 0.959753 test= -nan train= 0.3889857
rnd 307: wh-err= 0.999957 th-err= 0.959712 test= -nan train= 0.3888823
rnd 308: wh-err= 0.999957 th-err= 0.959670 test= -nan train= 0.3887877
rnd 309: wh-err= 0.999957 th-err= 0.959628 test= -nan train= 0.3887187
rnd 310: wh-err= 0.999957 th-err= 0.959587 test= -nan train= 0.3886305
rnd 311: wh-err= 0.999957 th-err= 0.959546 test= -nan train= 0.3885424
rnd 312: wh-err= 0.999957 th-err= 0.959505 test= -nan train= 0.3884384
rnd 313: wh-err= 0.999958 th-err= 0.959464 test= -nan train= 0.3884118
rnd 314: wh-err= 0.999958 th-err= 0.959423 test= -nan train= 0.3884084
rnd 315: wh-err= 0.999958 th-err= 0.959383 test= -nan train= 0.3883118
rnd 316: wh-err= 0.999958 th-err= 0.959343 test= -nan train= 0.3883128
rnd 317: wh-err= 0.999959 th-err= 0.959303 test= -nan train= 0.3883133
rnd 318: wh-err= 0.999959 th-err= 0.959263 test= -nan train= 0.3883128
rnd 319: wh-err= 0.999959 th-err= 0.959224 test= -nan train= 0.3883079
rnd 320: wh-err= 0.999959 th-err= 0.959185 test= -nan train= 0.3882074
rnd 321: wh-err= 0.999959 th-err= 0.959145 test= -nan train= 0.3881207
rnd 322: wh-err= 0.999959 th-err= 0.959106 test= -nan train= 0.3881276
rnd 323: wh-err= 0.999957 th-err= 0.959065 test= -nan train= 0.3880458
rnd 324: wh-err= 0.999959 th-err= 0.959025 test= -nan train= 0.3879084
rnd 325: wh-err= 0.999959 th-err= 0.958986 test= -nan train= 0.3879069
rnd 326: wh-err= 0.999959 th-err= 0.958947 test= -nan train= 0.3878227
rnd 327: wh-err= 0.999959 th-err= 0.958908 test= -nan train= 0.3877606
rnd 328: wh-err= 0.999960 th-err= 0.958870 test= -nan train= 0.3877039
rnd 329: wh-err= 0.999960 th-err= 0.958831 test= -nan train= 0.3877005
rnd 330: wh-err= 0.999960 th-err= 0.958793 test= -nan train= 0.3876985
rnd 331: wh-err= 0.999960 th-err= 0.958754 test= -nan train= 0.3876167
rnd 332: wh-err= 0.999960 th-err= 0.958716 test= -nan train= 0.3874877
rnd 333: wh-err= 0.999960 th-err= 0.958678 test= -nan train= 0.3874877
rnd 334: wh-err= 0.999961 th-err= 0.958640 test= -nan train= 0.3873966
rnd 335: wh-err= 0.999961 th-err= 0.958602 test= -nan train= 0.3873256
rnd 336: wh-err= 0.999961 th-err= 0.958565 test= -nan train= 0.3872448
rnd 337: wh-err= 0.999961 th-err= 0.958527 test= -nan train= 0.3870833
rnd 338: wh-err= 0.999961 th-err= 0.958490 test= -nan train= 0.3869921
rnd 339: wh-err= 0.999961 th-err= 0.958452 test= -nan train= 0.3869281
rnd 340: wh-err= 0.999961 th-err= 0.958415 test= -nan train= 0.3868557
rnd 341: wh-err= 0.999961 th-err= 0.958377 test= -nan train= 0.3868552
rnd 342: wh-err= 0.999961 th-err= 0.958340 test= -nan train= 0.3868532
rnd 343: wh-err= 0.999961 th-err= 0.958303 test= -nan train= 0.3866099
rnd 344: wh-err= 0.999961 th-err= 0.958266 test= -nan train= 0.3865995
rnd 345: wh-err= 0.999962 th-err= 0.958229 test= -nan train= 0.3865034
rnd 346: wh-err= 0.999962 th-err= 0.958193 test= -nan train= 0.3864054
rnd 347: wh-err= 0.999962 th-err= 0.958156 test= -nan train= 0.3863128
rnd 348: wh-err= 0.999962 th-err= 0.958120 test= -nan train= 0.3862576
rnd 349: wh-err= 0.999962 th-err= 0.958083 test= -nan train= 0.3861990
rnd 350: wh-err= 0.999962 th-err= 0.958047 test= -nan train= 0.3861325
rnd 351: wh-err= 0.999959 th-err= 0.958008 test= -nan train= 0.3861335
rnd 352: wh-err= 0.999962 th-err= 0.957971 test= -nan train= 0.3858813
rnd 353: wh-err= 0.999962 th-err= 0.957935 test= -nan train= 0.3858798
rnd 354: wh-err= 0.999962 th-err= 0.957898 test= -nan train= 0.3858645
rnd 355: wh-err= 0.999962 th-err= 0.957862 test= -nan train= 0.3858468
rnd 356: wh-err= 0.999962 th-err= 0.957826 test= -nan train= 0.3858049
rnd 357: wh-err= 0.999962 th-err= 0.957790 test= -nan train= 0.3857970
rnd 358: wh-err= 0.999963 th-err= 0.957754 test= -nan train= 0.3857611
rnd 359: wh-err= 0.999960 th-err= 0.957716 test= -nan train= 0.3857640
rnd 360: wh-err= 0.999962 th-err= 0.957680 test= -nan train= 0.3856872
rnd 361: wh-err= 0.999962 th-err= 0.957644 test= -nan train= 0.3856867
rnd 362: wh-err= 0.999963 th-err= 0.957608 test= -nan train= 0.3856852
rnd 363: wh-err= 0.999963 th-err= 0.957572 test= -nan train= 0.3856823
rnd 364: wh-err= 0.999963 th-err= 0.957537 test= -nan train= 0.3856729
rnd 365: wh-err= 0.999963 th-err= 0.957501 test= -nan train= 0.3856468
rnd 366: wh-err= 0.999963 th-err= 0.957466 test= -nan train= 0.3856567
rnd 367: wh-err= 0.999963 th-err= 0.957431 test= -nan train= 0.3856054
rnd 368: wh-err= 0.999963 th-err= 0.957396 test= -nan train= 0.3854823
rnd 369: wh-err= 0.999963 th-err= 0.957360 test= -nan train= 0.3853596
rnd 370: wh-err= 0.999963 th-err= 0.957325 test= -nan train= 0.3852852
rnd 371: wh-err= 0.999963 th-err= 0.957290 test= -nan train= 0.3852433
rnd 372: wh-err= 0.999963 th-err= 0.957255 test= -nan train= 0.3852025
rnd 373: wh-err= 0.999957 th-err= 0.957214 test= -nan train= 0.3851852
rnd 374: wh-err= 0.999963 th-err= 0.957179 test= -nan train= 0.3851276
rnd 375: wh-err= 0.999964 th-err= 0.957144 test= -nan train= 0.3850542
rnd 376: wh-err= 0.999964 th-err= 0.957110 test= -nan train= 0.3849429
rnd 377: wh-err= 0.999964 th-err= 0.957075 test= -nan train= 0.3849094
rnd 378: wh-err= 0.999963 th-err= 0.957039 test= -nan train= 0.3849236
rnd 379: wh-err= 0.999963 th-err= 0.957004 test= -nan train= 0.3848571
rnd 380: wh-err= 0.999963 th-err= 0.956969 test= -nan train= 0.3847837
rnd 381: wh-err= 0.999964 th-err= 0.956934 test= -nan train= 0.3846985
rnd 382: wh-err= 0.999964 th-err= 0.956899 test= -nan train= 0.3846281
rnd 383: wh-err= 0.999964 th-err= 0.956864 test= -nan train= 0.3845355
rnd 384: wh-err= 0.999964 th-err= 0.956830 test= -nan train= 0.3844650
rnd 385: wh-err= 0.999964 th-err= 0.956795 test= -nan train= 0.3844281
rnd 386: wh-err= 0.999964 th-err= 0.956760 test= -nan train= 0.3843576
rnd 387: wh-err= 0.999964 th-err= 0.956726 test= -nan train= 0.3843217
rnd 388: wh-err= 0.999964 th-err= 0.956692 test= -nan train= 0.3842335
rnd 389: wh-err= 0.999964 th-err= 0.956657 test= -nan train= 0.3841635
rnd 390: wh-err= 0.999964 th-err= 0.956623 test= -nan train= 0.3840995
rnd 391: wh-err= 0.999964 th-err= 0.956589 test= -nan train= 0.3840059
rnd 392: wh-err= 0.999964 th-err= 0.956555 test= -nan train= 0.3839133
rnd 393: wh-err= 0.999965 th-err= 0.956521 test= -nan train= 0.3839113
rnd 394: wh-err= 0.999965 th-err= 0.956487 test= -nan train= 0.3839113
rnd 395: wh-err= 0.999965 th-err= 0.956454 test= -nan train= 0.3838123
rnd 396: wh-err= 0.999965 th-err= 0.956420 test= -nan train= 0.3837296
rnd 397: wh-err= 0.999965 th-err= 0.956386 test= -nan train= 0.3837286
rnd 398: wh-err= 0.999965 th-err= 0.956353 test= -nan train= 0.3836719
rnd 399: wh-err= 0.999965 th-err= 0.956319 test= -nan train= 0.3836562
rnd 400: wh-err= 0.999965 th-err= 0.956286 test= -nan train= 0.3835527
rnd 401: wh-err= 0.999965 th-err= 0.956253 test= -nan train= 0.3834847
rnd 402: wh-err= 0.999965 th-err= 0.956219 test= -nan train= 0.3834808
rnd 403: wh-err= 0.999965 th-err= 0.956186 test= -nan train= 0.3834803
rnd 404: wh-err= 0.999965 th-err= 0.956153 test= -nan train= 0.3834709
rnd 405: wh-err= 0.999965 th-err= 0.956120 test= -nan train= 0.3834163
rnd 406: wh-err= 0.999965 th-err= 0.956087 test= -nan train= 0.3832970
rnd 407: wh-err= 0.999965 th-err= 0.956054 test= -nan train= 0.3833094
rnd 408: wh-err= 0.999965 th-err= 0.956021 test= -nan train= 0.3832473
rnd 409: wh-err= 0.999966 th-err= 0.955988 test= -nan train= 0.3832113
rnd 410: wh-err= 0.999966 th-err= 0.955955 test= -nan train= 0.3832054
rnd 411: wh-err= 0.999966 th-err= 0.955922 test= -nan train= 0.3832054
rnd 412: wh-err= 0.999966 th-err= 0.955889 test= -nan train= 0.3831483
rnd 413: wh-err= 0.999966 th-err= 0.955856 test= -nan train= 0.3831532
rnd 414: wh-err= 0.999966 th-err= 0.955824 test= -nan train= 0.3830833
rnd 415: wh-err= 0.999966 th-err= 0.955791 test= -nan train= 0.3829783
rnd 416: wh-err= 0.999962 th-err= 0.955754 test= -nan train= 0.3830596
rnd 417: wh-err= 0.999966 th-err= 0.955721 test= -nan train= 0.3829291
rnd 418: wh-err= 0.999966 th-err= 0.955689 test= -nan train= 0.3828084
rnd 419: wh-err= 0.999966 th-err= 0.955656 test= -nan train= 0.3827517
rnd 420: wh-err= 0.999966 th-err= 0.955624 test= -nan train= 0.3826867
rnd 421: wh-err= 0.999966 th-err= 0.955592 test= -nan train= 0.3826882
rnd 422: wh-err= 0.999966 th-err= 0.955559 test= -nan train= 0.3827030
rnd 423: wh-err= 0.999965 th-err= 0.955526 test= -nan train= 0.3826936
rnd 424: wh-err= 0.999966 th-err= 0.955493 test= -nan train= 0.3825975
rnd 425: wh-err= 0.999966 th-err= 0.955461 test= -nan train= 0.3826015
rnd 426: wh-err= 0.999966 th-err= 0.955429 test= -nan train= 0.3826000
rnd 427: wh-err= 0.999967 th-err= 0.955397 test= -nan train= 0.3825975
rnd 428: wh-err= 0.999967 th-err= 0.955366 test= -nan train= 0.3824926
rnd 429: wh-err= 0.999967 th-err= 0.955334 test= -nan train= 0.3824916
rnd 430: wh-err= 0.999967 th-err= 0.955302 test= -nan train= 0.3824764
rnd 431: wh-err= 0.999967 th-err= 0.955271 test= -nan train= 0.3824355
rnd 432: wh-err= 0.999967 th-err= 0.955239 test= -nan train= 0.3824350
rnd 433: wh-err= 0.999967 th-err= 0.955208 test= -nan train= 0.3824163
rnd 434: wh-err= 0.999967 th-err= 0.955176 test= -nan train= 0.3823635
rnd 435: wh-err= 0.999967 th-err= 0.955145 test= -nan train= 0.3825631
rnd 436: wh-err= 0.999967 th-err= 0.955113 test= -nan train= 0.3824975
rnd 437: wh-err= 0.999967 th-err= 0.955082 test= -nan train= 0.3824975
rnd 438: wh-err= 0.999967 th-err= 0.955051 test= -nan train= 0.3824773
rnd 439: wh-err= 0.999967 th-err= 0.955020 test= -nan train= 0.3824773
rnd 440: wh-err= 0.999968 th-err= 0.954989 test= -nan train= 0.3823882
rnd 441: wh-err= 0.999968 th-err= 0.954958 test= -nan train= 0.3823867
rnd 442: wh-err= 0.999968 th-err= 0.954927 test= -nan train= 0.3823015
rnd 443: wh-err= 0.999968 th-err= 0.954896 test= -nan train= 0.3823010
rnd 444: wh-err= 0.999968 th-err= 0.954865 test= -nan train= 0.3821941
rnd 445: wh-err= 0.999968 th-err= 0.954834 test= -nan train= 0.3821867
rnd 446: wh-err= 0.999968 th-err= 0.954803 test= -nan train= 0.3821921
rnd 447: wh-err= 0.999954 th-err= 0.954759 test= -nan train= 0.3818793
rnd 448: wh-err= 0.999968 th-err= 0.954728 test= -nan train= 0.3818128
rnd 449: wh-err= 0.999968 th-err= 0.954698 test= -nan train= 0.3817966
rnd 450: wh-err= 0.999968 th-err= 0.954667 test= -nan train= 0.3817961
rnd 451: wh-err= 0.999968 th-err= 0.954636 test= -nan train= 0.3818094
rnd 452: wh-err= 0.999968 th-err= 0.954606 test= -nan train= 0.3817872
rnd 453: wh-err= 0.999968 th-err= 0.954575 test= -nan train= 0.3817325
rnd 454: wh-err= 0.999968 th-err= 0.954545 test= -nan train= 0.3816729
rnd 455: wh-err= 0.999968 th-err= 0.954514 test= -nan train= 0.3816044
rnd 456: wh-err= 0.999968 th-err= 0.954484 test= -nan train= 0.3815576
rnd 457: wh-err= 0.999968 th-err= 0.954454 test= -nan train= 0.3814985
rnd 458: wh-err= 0.999968 th-err= 0.954424 test= -nan train= 0.3814478
rnd 459: wh-err= 0.999969 th-err= 0.954394 test= -nan train= 0.3814025
rnd 460: wh-err= 0.999969 th-err= 0.954364 test= -nan train= 0.3812798
rnd 461: wh-err= 0.999969 th-err= 0.954334 test= -nan train= 0.3812719
rnd 462: wh-err= 0.999969 th-err= 0.954304 test= -nan train= 0.3812108
rnd 463: wh-err= 0.999969 th-err= 0.954274 test= -nan train= 0.3811778
rnd 464: wh-err= 0.999969 th-err= 0.954244 test= -nan train= 0.3811749
rnd 465: wh-err= 0.999969 th-err= 0.954215 test= -nan train= 0.3811734
rnd 466: wh-err= 0.999969 th-err= 0.954185 test= -nan train= 0.3811163
rnd 467: wh-err= 0.999969 th-err= 0.954155 test= -nan train= 0.3810665
rnd 468: wh-err= 0.999969 th-err= 0.954126 test= -nan train= 0.3810005
rnd 469: wh-err= 0.999969 th-err= 0.954096 test= -nan train= 0.3809966
rnd 470: wh-err= 0.999969 th-err= 0.954067 test= -nan train= 0.3809961
rnd 471: wh-err= 0.999969 th-err= 0.954037 test= -nan train= 0.3809429
rnd 472: wh-err= 0.999969 th-err= 0.954008 test= -nan train= 0.3809399
rnd 473: wh-err= 0.999969 th-err= 0.953978 test= -nan train= 0.3809399
rnd 474: wh-err= 0.999969 th-err= 0.953949 test= -nan train= 0.3808778
rnd 475: wh-err= 0.999969 th-err= 0.953920 test= -nan train= 0.3808394
rnd 476: wh-err= 0.999969 th-err= 0.953891 test= -nan train= 0.3807754
rnd 477: wh-err= 0.999969 th-err= 0.953861 test= -nan train= 0.3806818
rnd 478: wh-err= 0.999969 th-err= 0.953832 test= -nan train= 0.3806310
rnd 479: wh-err= 0.999969 th-err= 0.953803 test= -nan train= 0.3805670
rnd 480: wh-err= 0.999969 th-err= 0.953774 test= -nan train= 0.3804837
rnd 481: wh-err= 0.999970 th-err= 0.953745 test= -nan train= 0.3804823
rnd 482: wh-err= 0.999970 th-err= 0.953716 test= -nan train= 0.3804823
rnd 483: wh-err= 0.999970 th-err= 0.953687 test= -nan train= 0.3804808
rnd 484: wh-err= 0.999970 th-err= 0.953658 test= -nan train= 0.3804783
rnd 485: wh-err= 0.999970 th-err= 0.953629 test= -nan train= 0.3804852
rnd 486: wh-err= 0.999970 th-err= 0.953600 test= -nan train= 0.3804340
rnd 487: wh-err= 0.999970 th-err= 0.953572 test= -nan train= 0.3804320
rnd 488: wh-err= 0.999970 th-err= 0.953543 test= -nan train= 0.3804315
rnd 489: wh-err= 0.999970 th-err= 0.953515 test= -nan train= 0.3804300
rnd 490: wh-err= 0.999970 th-err= 0.953486 test= -nan train= 0.3802365
rnd 491: wh-err= 0.999970 th-err= 0.953458 test= -nan train= 0.3801448
rnd 492: wh-err= 0.999970 th-err= 0.953429 test= -nan train= 0.3800493
rnd 493: wh-err= 0.999970 th-err= 0.953401 test= -nan train= 0.3800217
rnd 494: wh-err= 0.999970 th-err= 0.953372 test= -nan train= 0.3799823
rnd 495: wh-err= 0.999970 th-err= 0.953344 test= -nan train= 0.3799207
rnd 496: wh-err= 0.999970 th-err= 0.953316 test= -nan train= 0.3798581
rnd 497: wh-err= 0.999970 th-err= 0.953287 test= -nan train= 0.3797133
rnd 498: wh-err= 0.999966 th-err= 0.953255 test= -nan train= 0.3797094
rnd 499: wh-err= 0.999968 th-err= 0.953224 test= -nan train= 0.3796478
rnd 500: wh-err= 0.999970 th-err= 0.953196 test= -nan train= 0.3796271
rnd 501: wh-err= 0.999970 th-err= 0.953168 test= -nan train= 0.3795537
rnd 502: wh-err= 0.999970 th-err= 0.953139 test= -nan train= 0.3794946
rnd 503: wh-err= 0.999970 th-err= 0.953111 test= -nan train= 0.3794015
rnd 504: wh-err= 0.999971 th-err= 0.953083 test= -nan train= 0.3793586
rnd 505: wh-err= 0.999971 th-err= 0.953055 test= -nan train= 0.3794429
rnd 506: wh-err= 0.999971 th-err= 0.953027 test= -nan train= 0.3794310
rnd 507: wh-err= 0.999971 th-err= 0.952999 test= -nan train= 0.3793660
rnd 508: wh-err= 0.999971 th-err= 0.952971 test= -nan train= 0.3793626
rnd 509: wh-err= 0.999971 th-err= 0.952944 test= -nan train= 0.3792941
rnd 510: wh-err= 0.999971 th-err= 0.952916 test= -nan train= 0.3792857
rnd 511: wh-err= 0.999971 th-err= 0.952889 test= -nan train= 0.3793177
rnd 512: wh-err= 0.999971 th-err= 0.952861 test= -nan train= 0.3792507
rnd 513: wh-err= 0.999971 th-err= 0.952834 test= -nan train= 0.3792581
rnd 514: wh-err= 0.999971 th-err= 0.952807 test= -nan train= 0.3792271
rnd 515: wh-err= 0.999971 th-err= 0.952779 test= -nan train= 0.3791581
rnd 516: wh-err= 0.999971 th-err= 0.952752 test= -nan train= 0.3789734
rnd 517: wh-err= 0.999971 th-err= 0.952725 test= -nan train= 0.3787330
rnd 518: wh-err= 0.999970 th-err= 0.952696 test= -nan train= 0.3788488
rnd 519: wh-err= 0.999971 th-err= 0.952669 test= -nan train= 0.3788044
rnd 520: wh-err= 0.999971 th-err= 0.952641 test= -nan train= 0.3787837
rnd 521: wh-err= 0.999972 th-err= 0.952614 test= -nan train= 0.3787823
rnd 522: wh-err= 0.999972 th-err= 0.952587 test= -nan train= 0.3787798
rnd 523: wh-err= 0.999972 th-err= 0.952560 test= -nan train= 0.3787788
rnd 524: wh-err= 0.999972 th-err= 0.952533 test= -nan train= 0.3786990
rnd 525: wh-err= 0.999972 th-err= 0.952506 test= -nan train= 0.3787143
rnd 526: wh-err= 0.999972 th-err= 0.952479 test= -nan train= 0.3786527
rnd 527: wh-err= 0.999972 th-err= 0.952452 test= -nan train= 0.3785985
rnd 528: wh-err= 0.999972 th-err= 0.952426 test= -nan train= 0.3785872
rnd 529: wh-err= 0.999972 th-err= 0.952399 test= -nan train= 0.3785532
rnd 530: wh-err= 0.999972 th-err= 0.952372 test= -nan train= 0.3784232
rnd 531: wh-err= 0.999972 th-err= 0.952345 test= -nan train= 0.3783547
rnd 532: wh-err= 0.999972 th-err= 0.952319 test= -nan train= 0.3782936
rnd 533: wh-err= 0.999972 th-err= 0.952292 test= -nan train= 0.3782931
rnd 534: wh-err= 0.999972 th-err= 0.952266 test= -nan train= 0.3782926
rnd 535: wh-err= 0.999972 th-err= 0.952239 test= -nan train= 0.3782586
rnd 536: wh-err= 0.999972 th-err= 0.952213 test= -nan train= 0.3782158
rnd 537: wh-err= 0.999972 th-err= 0.952187 test= -nan train= 0.3782123
rnd 538: wh-err= 0.999972 th-err= 0.952160 test= -nan train= 0.3781478
rnd 539: wh-err= 0.999972 th-err= 0.952134 test= -nan train= 0.3781409
rnd 540: wh-err= 0.999972 th-err= 0.952108 test= -nan train= 0.3781118
rnd 541: wh-err= 0.999972 th-err= 0.952082 test= -nan train= 0.3780724
rnd 542: wh-err= 0.999972 th-err= 0.952055 test= -nan train= 0.3780724
rnd 543: wh-err= 0.999972 th-err= 0.952029 test= -nan train= 0.3780626
rnd 544: wh-err= 0.999972 th-err= 0.952003 test= -nan train= 0.3780099
rnd 545: wh-err= 0.999973 th-err= 0.951977 test= -nan train= 0.3780074
rnd 546: wh-err= 0.999973 th-err= 0.951951 test= -nan train= 0.3780059
rnd 547: wh-err= 0.999973 th-err= 0.951925 test= -nan train= 0.3779626
rnd 548: wh-err= 0.999973 th-err= 0.951899 test= -nan train= 0.3779113
rnd 549: wh-err= 0.999973 th-err= 0.951873 test= -nan train= 0.3778502
rnd 550: wh-err= 0.999973 th-err= 0.951847 test= -nan train= 0.3778360
rnd 551: wh-err= 0.999973 th-err= 0.951822 test= -nan train= 0.3777828
rnd 552: wh-err= 0.999973 th-err= 0.951796 test= -nan train= 0.3777251
rnd 553: wh-err= 0.999973 th-err= 0.951770 test= -nan train= 0.3777232
rnd 554: wh-err= 0.999973 th-err= 0.951744 test= -nan train= 0.3777227
rnd 555: wh-err= 0.999973 th-err= 0.951718 test= -nan train= 0.3776970
rnd 556: wh-err= 0.999973 th-err= 0.951693 test= -nan train= 0.3776803
rnd 557: wh-err= 0.999973 th-err= 0.951667 test= -nan train= 0.3775330
rnd 558: wh-err= 0.999970 th-err= 0.951639 test= -nan train= 0.3775241
rnd 559: wh-err= 0.999970 th-err= 0.951611 test= -nan train= 0.3775099
rnd 560: wh-err= 0.999972 th-err= 0.951585 test= -nan train= 0.3774916
rnd 561: wh-err= 0.999973 th-err= 0.951558 test= -nan train= 0.3774773
rnd 562: wh-err= 0.999973 th-err= 0.951532 test= -nan train= 0.3774562
rnd 563: wh-err= 0.999973 th-err= 0.951506 test= -nan train= 0.3774394
rnd 564: wh-err= 0.999973 th-err= 0.951480 test= -nan train= 0.3774034
rnd 565: wh-err= 0.999973 th-err= 0.951455 test= -nan train= 0.3773631
rnd 566: wh-err= 0.999973 th-err= 0.951429 test= -nan train= 0.3773059
rnd 567: wh-err= 0.999973 th-err= 0.951403 test= -nan train= 0.3773059
rnd 568: wh-err= 0.999973 th-err= 0.951378 test= -nan train= 0.3772631
rnd 569: wh-err= 0.999973 th-err= 0.951352 test= -nan train= 0.3772044
rnd 570: wh-err= 0.999973 th-err= 0.951327 test= -nan train= 0.3772197
rnd 571: wh-err= 0.999973 th-err= 0.951301 test= -nan train= 0.3771468
rnd 572: wh-err= 0.999973 th-err= 0.951276 test= -nan train= 0.3771458
Worker killed run: Current time usage 5h0m exceeds allowed 5h0m
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) binary-to-multi : Allows multiclass classification to be run on binary classification datasets (trivial reduction).
(multiclassLearner:Program[MulticlassClassification]) boostexter : Adaboost on single level decision trees. AT&T's closed-source implementation. See http://www.cs.princeton.edu/~schapire/boostexter.html.
(dataset:Dataset) testingbig : big testing file
(stripper:Program[Strip]) binary-utils : Validates and inspects a dataset in BinaryClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
exitCode: -1
success: false
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