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    # auc    param['eval_metric'] = 'auc'

1. 
   0.661716221418

    # setting patameters    param = {'bst:subsample':0.8, 'bst:min_child_weight':1, 'silent':1,'objective':'binary:logistic'}    param['nthread'] = 2    # banlance    #param['scale_pos_weight'] = 1    # auc    param['eval_metric'] = 'auc'    # important feature    param['bst:max_depth'] = 6    param['bst:min_child_weight'] = 1    param['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 patameters    param = {'bst:subsample':0.8, 'bst:min_child_weight':1, 'silent':1,'objective':'binary:logistic'}    param['nthread'] = 2    # banlance    #param['scale_pos_weight'] = 1    # auc    param['eval_metric'] = 'auc'    # important feature    param['bst:max_depth'] = 10    param['bst:min_child_weight'] = 1    param['bst:eta'] = 0.3    # cross validation    #cross_validation(DATA_PATH+"processed",param)    # num_round    num_round = 50

1. 增加label

[Dimension]idfa_names=id,city,street,system_info,version,dpi,tag1,tag2,tag3,tag4,tag5,tag6,tag7imei_names=id,androidid,mac,city,street,system_info,version,dpi,tag1,tag2,tag3,tag4,tag5,tag6,tag7[Parameter]extention=1processed_name=processed_extention.datamodel_name=0001.modelnthread=3max_depth=20min_child_weight=1eta=0.3num_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:0.679658     feature  code208  2011117   166223  2011301    97230  2011308    9219   1010301    91203  2011112    83222    20113    7699     10305    74156    20107    73152  2010601    69244  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