xgboost note

参数记录

param = {
   'bst:max_depth':3, 'bst:subsample':0.5, 'bst:min_child_weight':1,'bst:eta':0.3, 'silent':1,'objective':'binary:logistic'}
param['nthread'] = 2

1. 50 iter ：ａｕｃ：0.661716221418

param = {
   'bst:max_depth':3, 'bst:subsample':0.8, 'bst:min_child_weight':1,'bst:eta':0.01, 'silent':1,'objective':'binary:logistic'}param['nthread'] = 2# banlance#param['scale_pos_weight'] = 1# aucparam['eval_metric'] = 'auc'

1. 
   0.661716221418

# setting patametersparam = {
   'bst:subsample':0.8, 'bst:min_child_weight':1, 'silent':1,'objective':'binary:logistic'}param['nthread'] = 2# banlance#param['scale_pos_weight'] = 1# aucparam['eval_metric'] = 'auc'# important featureparam['bst:max_depth'] = 6param['bst:min_child_weight'] = 1param['bst:eta'] = 0.1# cross validation#cross_validation(DATA_PATH+"processed",param)# num_round

1. 
   [49] eval-auc:0.661716 train-auc:0.670260

# setting patametersparam = {
   'bst:subsample':0.8, 'bst:min_child_weight':1, 'silent':1,'objective':'binary:logistic'}param['nthread'] = 2# banlance#param['scale_pos_weight'] = 1# aucparam['eval_metric'] = 'auc'# important featureparam['bst:max_depth'] = 10param['bst:min_child_weight'] = 1param['bst:eta'] = 0.3# cross validation#cross_validation(DATA_PATH+"processed",param)# num_roundnum_round = 50

1. 增加label

[Dimension]
idfa_names=id,city,street,system_info,version,dpi,tag1,tag2,tag3,tag4,tag5,tag6,tag7
imei_names=id,androidid,mac,city,street,system_info,version,dpi,tag1,tag2,tag3,tag4,tag5,tag6,tag7[Parameter]
extention=1
processed_name=processed_extention.data
model_name=0001.model
nthread=3
max_depth=20
min_child_weight=1
eta=0.3
num_round=100
~
~

[94]    eval-auc:0.660827       train-auc:0.679268
[95]    eval-auc:0.660816       train-auc:0.679359
[96]    eval-auc:0.660762       train-auc:0.679443
[97]    eval-auc:0.660748       train-auc:0.679526
[98]    eval-auc:0.660743       train-auc:0.679559
[99]    eval-auc:0.660754       train-auc:0.679658feature  code
208  2011117   166
223  2011301    97
230  2011308    92
19   1010301    91
203  2011112    83
222    20113    76
99     10305    74
156    20107    73
152  2010601    69
244  2011411    68

5。linear 无正则化

[95]    eval-auc:0.623535       train-auc:0.623284
[96]    eval-auc:0.623539       train-auc:0.623282
[97]    eval-auc:0.623535       train-auc:0.623281
[98]    eval-auc:0.623536       train-auc:0.623280
[99]    eval-auc:0.623534       train-auc:0.623279