比赛得分公式如下：
其中，P为Precision , R为 Recall。
GBDT训练基于验证集评价，此时会调用评价函数，XGBoost的best_iteration和best_score均是基于评价函数得出。
评价函数：
input: preds和dvalid，即为验证集和验证集上的预测值，
return string 类型的名称 和一个flaot类型的fevalerror值表示评价值的大小，其是以error的形式定义，即当此值越大是认为模型效果越差。
1 from sklearn.metrics import confusion_matrix 2 def customedscore(preds, dtrain): 3 label = dtrain.get_label() 4 pred = [int(i>=0.5) for i in preds] 5 confusion_matrixs = confusion_matrix(label, pred) 6 recall =float(confusion_matrixs[0][0]) / float(confusion_matrixs[0][1]+confusion_matrixs[0][0]) 7 precision = float(confusion_matrixs[0][0]) / float(confusion_matrixs[1][0]+confusion_matrixs[0][0]) 8 F = 5*precision* recall/(2*precision+3*recall)*100 9 return 'FSCORE',float(F)
应用：
训练时要传入参数：feval = customedscore,
1 params = { 'silent': 1, 'objective': 'binary:logistic' , 'gamma':0.1, 2 'min_child_weight':5, 3 'max_depth':5, 4 'lambda':10, 5 'subsample':0.7, 6 'colsample_bytree':0.7, 7 'colsample_bylevel':0.7, 8 'eta': 0.01, 9 'tree_method':'exact'} 10 model = xgb.train(params, trainsetall, num_round,verbose_eval=10, feval = customedscore,maximize=False)
自定义 目标函数，这个我没有具体使用
1 # user define objective function, given prediction, return gradient and second order gradient 2 # this is log likelihood loss 3 def logregobj(preds, dtrain): 4 labels = dtrain.get_label() 5 preds = 1.0 / (1.0 + np.exp(-preds)) 6 grad = preds - labels 7 hess = preds * (1.0-preds) 8 return grad, hess
# training with customized objective, we can also do step by step training # simply look at xgboost.py's implementation of train bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
参考：
https://github.com/dmlc/xgboost/blob/master/demo/guide-python/custom_objective.py
http://blog.csdn.net/lujiandong1/article/details/52791117