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

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

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数据集简介

Dataset之HiggsBoson：Higgs Boson(Kaggle竞赛)数据集的简介、下载、案例应用之详细攻略

输出结果

更新中……

1、交叉训练时间比较长，大约需要20多分钟。

设计思路

更新中……

核心代码

更新中……

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')