ML之xgboost：基于xgboost(5f-CrVa)算法对HiggsBoson数据集(Kaggle竞赛)训练实现二分类预测(基于训练好的模型进行新数据预测)
目录
输出结果
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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
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
设计思路
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核心代码
更新中……
xgmat = xgb.DMatrix( data, missing = -999.0 )
bst = xgb.Booster({'nthread':8}, model_file = modelfile)
res = [ ( int(idx[i]), ypred[i] ) for i in range(len(ypred)) ]
rorder = {}
for k, v in sorted( res, key = lambda x:-x[1] ):
rorder[ k ] = len(rorder) + 1
# write out predictions
ntop = int( threshold_ratio * len(rorder ) )
fo = open(outfile, 'w')
nhit = 0
ntot = 0
fo.write('EventId,RankOrder,Class\n')
for k, v in res:
if rorder[k] <= ntop:
lb = 's'
nhit += 1
else:
lb = 'b'
# change output rank order to follow Kaggle convention
fo.write('%s,%d,%s\n' % ( k, len(rorder)+1-rorder[k], lb ) )
ntot += 1
fo.close()
