why default C = 300 used in mitie for NER training ?

[munaAchyuta]

need help ...

sorry guys it's not exactly issue. but it's information i need..

guys please tell me why default C = 300 used in mitie ? when we are doing cross validation with taking input min_C = 0.0001 , max_C = 5000 and epsilon = 1. i believe we are using cross validation for choosing best hyper parameter(C & epsilon). then what is the use of defining C=300 ?

i know what is the use of C (regularisation hyper parameter.)

my problem is : i tried with different value of C but keeping other value as it is. and every time i got same Accuracy and F1 score with different best C value. why is it so ?

please see log ..

=============================================== C=300
num training samples: 1441
C: 200 f-score: 0.734335
C: 400 f-score: 0.735081
C: 300 f-score: 0.731994
C: 500 f-score: 0.735241
C: 700 f-score: 0.734709
C: 520 f-score: 0.733273
C: 450.957 f-score: 0.733804
C: 483.4 f-score: 0.736308
C: 480.156 f-score: 0.735241
C: 490.078 f-score: 0.734653
C: 484.607 f-score: 0.735241
C: 482.381 f-score: 0.732305
C: 483.799 f-score: 0.734653
C: 483.236 f-score: 0.732149
best C: 483.4

test on train:
286 2 0 3
0 759 0 3
0 0 43 0
4 6 0 335

overall accuracy: 0.987509
Part II: elapsed time: 19417 seconds.
============================================== C=100
num training samples: 1420
C: 0.01 f-score: 0.673219
C: 200 f-score: 0.75807
C: 100 f-score: 0.758977
C: 148.954 f-score: 0.758783
C: 124.134 f-score: 0.759333
C: 121.721 f-score: 0.757521
C: 136.154 f-score: 0.760752
C: 134.952 f-score: 0.756639
C: 142.253 f-score: 0.757164
C: 138.668 f-score: 0.758945
C: 137.088 f-score: 0.756806
C: 136.031 f-score: 0.759333
C: 136.479 f-score: 0.759459
best C: 136.154
test on train:
286 2 0 3
0 761 0 1
0 0 43 0
4 9 0 311

overall accuracy: 0.98662
Part II: elapsed time: 6148 seconds.
============================================== C=50
num training samples: 1432
C: 0.01 f-score: 0.670678
C: 200 f-score: 0.754349
C: 100 f-score: 0.755016
C: 149.215 f-score: 0.753461
C: 121.914 f-score: 0.755938
C: 118.753 f-score: 0.753097
C: 134.168 f-score: 0.75631
C: 129.929 f-score: 0.756474
C: 129.128 f-score: 0.755917
C: 131.916 f-score: 0.754349
C: 130.128 f-score: 0.755402
C: 129.586 f-score: 0.755938
best C: 129.929
test on train:
286 2 0 3
0 761 0 1
0 0 43 0
5 10 0 321

overall accuracy: 0.985335
Part II: elapsed time: 5562 seconds.
df.number_of_classes(): 4
============================================== C=300
num training samples: 1455
C: 200 f-score: 0.73822
C: 400 f-score: 0.736475
C: 300 f-score: 0.738895
C: 271.805 f-score: 0.737705
C: 326.638 f-score: 0.735243
C: 292.355 f-score: 0.738378
C: 302.664 f-score: 0.733705
C: 296.35 f-score: 0.736475
C: 298.977 f-score: 0.737146
C: 300.35 f-score: 0.736944
C: 299.649 f-score: 0.738933
C: 299.804 f-score: 0.735961
best C: 299.649
test on train:
288 2 0 1
0 760 0 2
0 0 43 0
5 8 0 346

overall accuracy: 0.987629
Part II: elapsed time: 11576 seconds.
df.number_of_classes(): 4

============================================== C=500
Part II: train segment classifier
now do training
num training samples: 1358
PART-II C: 500
PART-II epsilon: 0.0001
PART-II num threads: 4
PART-II max iterations: 2000
C: 400 f-score: 0.774171
C: 600 f-score: 0.778615
C: 500 f-score: 0.779291
C: 538.343 f-score: 0.774471
C: 470.021 f-score: 0.779522
C: 480.425 f-score: 0.776386
C: 443.145 f-score: 0.774217
C: 463.96 f-score: 0.775954
C: 472.435 f-score: 0.775831
C: 468.168 f-score: 0.770751
C: 470.707 f-score: 0.772416
C: 469.493 f-score: 0.770333
C: 470.138 f-score: 0.779291
best C: 470.021
test on train:
287 2 0 2
0 761 0 1
0 0 43 0
6 9 0 247

overall accuracy: 0.985272
Part II: elapsed time: 18762 seconds.
df.number_of_classes(): 4

==============================================

from above log : why best C is coming nearer value of given "C" value ? no matter what C value i choose.

Thanks in advance. @baali @scotthaleen @avitale @kecsap @lopuhin @davisking @jinyichao @avitale