# h2o random forest training error metrics not decreasing?

I have trained h2o.randomforest model on my data and following is the scoring history.

 coring History:
timestamp          duration number_of_trees training_MSE     training_logloss training_AUC training_classification_error
1  2015-09-23 03:20:54        19.057 sec               1      0.49373          2.98436      0.89528                       0.18939
2  2015-09-23 03:21:02        26.870 sec               2      0.49292           2.95248      0.90202                       0.18089
3  2015-09-23 03:21:10        34.995 sec               3      0.49328          2.94815      0.90395                       0.17823
4  2015-09-23 03:21:18        43.356 sec               4      0.49291          2.94215      0.90454                       0.17665
5  2015-09-23 03:21:27        51.810 sec               5      0.49304          2.94646      0.90477                       0.17903
6  2015-09-23 03:21:42  1 min  7.623 sec               7      0.49322          2.94274      0.90646                       0.17785
7  2015-09-23 03:22:07  1 min 31.859 sec              10      0.49343          2.94660      0.90674                       0.17827
8  2015-09-23 03:22:39  2 min  4.409 sec              14      0.49347          2.95197      0.90752                       0.17472
9  2015-09-23 03:23:22  2 min 46.928 sec              19      0.49357          2.95385      0.90904                       0.17446
10 2015-09-23 03:24:12  3 min 37.249 sec              25      0.49386          2.96022      0.91061                       0.17231
11 2015-09-23 03:25:12  4 min 37.707 sec              32      0.49395          2.95995      0.91318                       0.16912
12 2015-09-23 03:26:19  5 min 44.072 sec              40      0.49411          2.96468      0.91505                       0.16655
13 2015-09-23 03:27:26  6 min 51.658 sec              48      0.49421          2.96624      0.91729                       0.16374
14 2015-09-23 03:28:41  8 min  6.478 sec              57      0.49431          2.96732      0.91982                       0.16102
15 2015-09-23 03:29:55  9 min 20.593 sec              66      0.49433          2.96980      0.92148                       0.15833
16 2015-09-23 03:31:21 10 min 46.086 sec              76      0.49437          2.97340      0.92272                       0.15528
17 2015-09-23 03:32:44 12 min  9.201 sec              86      0.49445          2.97545      0.92410                       0.15492
18 2015-09-23 03:34:07 13 min 32.039 sec              96      0.49449          2.97710      0.92534                       0.15165
19 2015-09-23 03:34:45 14 min  9.934 sec             100      0.49449          2.97725      0.92576                       0.15191


My question is that intuitively training MSE or training logloss should decrease as trees grow (basically error should decrease) but it is not happening here. Though AUC is increasing and classification error is decreasing as trees grow which is a good thing but why not training MSE or logloss? Is it a concern? Does it mean model is not behaving well on the data set?

Follwing are the parameter settings.

model_rf <- h2o.randomForest(x = predictors, y = labels,
training_frame = train_data,
ntree = 100,
mtries = 2,
nbins = 50,
sample_rate = 0.95,
keep_cross_validation_predictions = T,
binomial_double_trees = F,
min_rows = 5000,
balance_classes = T,
max_depth = 12)
#nfolds = 2)


My problem is a classification problem.

If any random forest guru could help me out here, that would be great!