### 团队:Unreal
### Rank:10
### 代码说明
#### data_pre_process
1.origin_part1_part2_row2col:进行原始数据转换,包括行转列,去重等;
2. get_num_features,生成数值特征的表;
3. get_word_features,生成文字特征的表;
4. data_merge_split,合并数值、文字以及snp数据。
#### feature_selection
1. 分别针对sys, dia, tl, hdl, ldl 运行snp_drop_one_hot, 得出五个对应特征的数据集,这一步骤主要是删去gbdt预训练中不重要的snp特征,然后进行one_hot编码;
2. 分别针对sys, dia, tl, hdl, ldl 运行get_best_rounds, 得出a步骤五个数据对应的五折最优迭代次数。
#### every_prediction_model
运行所有文件,得出sys,dia,tl,hdl,ldl在测试集上的预测结果。
#### xgb_model
1. add_prefix_for_xgb_model,得出带有前缀的特征数据集;
2. split_5_fold_data_xgb,分割五折训练的数据;
3. baseline_xgboost_jz,训练xgb模型;
4. calc_xgb_test_loss_and_save, 将c步骤中的五个tl的预测结果融合并取均值。
#### submit_result
提交最终结果,最终结果是sys,dia,hdl,ldl为gbdt单模型,tl为gbdt和xgb的加权融合,比例为0.7和0.35。
A榜单模型GBDT最优得分为0.0318,B榜单模型GBDT最优0.0321,tl加权融合后最优成绩0.0319。
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温馨提示
【作品名称】:美年健康AI大赛—基于 Python实现双高疾病风险预测 【适用人群】:适用于希望学习不同技术领域的小白或进阶学习者。可作为毕设项目、课程设计、大作业、工程实训或初期项目立项。 【项目介绍】:美年健康AI大赛—基于 Python实现双高疾病风险预测
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