我已将 XGBoost 的 eval 函数更改为 rmsle，并且优化在迭代 [2] 后增加了误差，而不是减少了误差。如果我更改为默认的 eval 函数 RMSE，则不会发生这种情况。
这是使用的 RMSLE 代码：
def evalerror(preds, dtrain): # this is compatible with DMatrix
labels = dtrain.get_label()
assert len(preds) == len(labels)
labels = labels.tolist()
preds = preds.tolist()
terms_to_sum = [(math.log(labels[i] + 1) - math.log(max(0,preds[i]) + 1)) ** 2.0 for i,pred in enumerate(labels)]
return 'error', (sum(terms_to_sum) * (1.0/len(preds))) ** 0.5
这是使用的 XGBoost 的参数：
param = {'bst:max_depth':1, 'bst:eta':0.025, 'silent':False, 'objective':'reg:linear','eval_metric':'rmse' }
bst = xgb.train( param, d_train, num_rounds,early_stopping_rounds=20, evals=eval_list, verbose_eval=True, feval=evalerror)
这是评估：
[0] eval-error:0.836219 train-error:0.835095
Multiple eval metrics have been passed: 'train-error' will be used for early stopping.
Will train until train-error hasn't improved in 20 rounds.
[1] eval-error:0.809301 train-error:0.806747
[2] eval-error:0.792647 train-error:0.78908
[3] eval-error:0.803355 train-error:0.798805
[4] eval-error:0.803261 train-error:0.79835
[5] eval-error:0.809352 train-error:0.804283
[6] eval-error:0.810453 train-error:0.805126
[7] eval-error:0.811059 train-error:0.805646
[8] eval-error:0.815261 train-error:0.809722
[9] eval-error:0.820237 train-error:0.814521
[10] eval-error:0.823378 train-error:0.817408
[11] eval-error:0.824981 train-error:0.81868
[12] eval-error:0.826607 train-error:0.820176
[13] eval-error:0.827813 train-error:0.821358
[14] eval-error:0.827625 train-error:0.821007
[15] eval-error:0.823347 train-error:0.816547
[16] eval-error:0.824362 train-error:0.81752
[17] eval-error:0.82529 train-error:0.818321
[18] eval-error:0.824621 train-error:0.817463
[19] eval-error:0.824103 train-error:0.816766
[20] eval-error:0.814759 train-error:0.807234
[21] eval-error:0.807961 train-error:0.800186
[22] eval-error:0.808398 train-error:0.800246
Stopping. Best iteration:
[2] eval-error:0.792647 train-error:0.78908
可能是我需要将我的目标函数调整为这个评估指标？