ML之xgboost：基于xgboost(5f-CrVa)算法对HiggsBoson数据集(Kaggle竞赛)训练(模型保存+可视化)实现二分类预测

输出结果

设计思路

核心代码

num_round = 1000                      

n_estimators = cvresult.shape[0]    

print ('running cross validation, with preprocessing function')

# do cross validation, for each fold

# the dtrain, dtest, param will be passed into fpreproc

# then the return value of fpreproc will be used to generate results of that fold

cvresult = xgb.cv(param, dtrain, num_round, nfold=5,    

                 metrics={'ams@0.15', 'auc'},        

                 early_stopping_rounds=10, seed = 0,

                 fpreproc = fpreproc)                    

print ('finish cross validation','\n',cvresult)  

print ('train model using the best parameters by cv ... ')

bst = xgb.train( param, dtrain, n_estimators )                        

bst.save_model('data_input/xgboost/data_higgsboson/higgs_cv.model')