Kaggle竞赛代码.zip_根据土壤选择合适的农作物kaggle资源-CSDN文库

共68个文件

png：29个

csv：26个

ipynb：6个

需积分: 5 67 浏览量 2024-01-14 12:55:37 上传评论收藏 3.17MB ZIP 举报

资源推荐

资源详情

资源评论

收起资源包目录

Kaggle 竞赛代码.zip （68个子文件）

SJT-code

House Price

data

sample_submission.csv 31KB

data_description.txt 13KB

train.csv 450KB

submission.csv 34KB

test.csv 441KB

FinalResult.png 17KB

README.md 1KB

code

House Prices：Advanced Regression Techniques.ipynb 1012KB

House+Prices：Advanced+Regression+Techniques.html 1.29MB

result

submission.csv 34KB

Titanic

data

test_preprocessed.csv 51KB

train_preprocessed.csv 108KB

train.csv 59KB

test.csv 28KB

FinalResult.png 43KB

README.md 1KB

code

Logit_Svm.ipynb 32KB

Ensemble.ipynb 6KB

Modeling&FeatureSelection.ipynb 425KB

FeaturePreprocessing.ipynb 256KB

result

xgb0_prediction.csv 3KB

benchmark.csv 4KB

gbm0_prediction.csv 3KB

rf0_prediction.csv 3KB

ensemble3.csv 3KB

xgb0_fs_prediction.csv 3KB

rf0_fs_prediction.csv 3KB

ensemble1.csv 3KB

ensemble.csv 3KB

rf_tuning_prediction.csv 3KB

rf0_rfecv_addfeature_prediction.csv 3KB

gbm0.csv 3KB

rf_tuning_classweight_prediction.csv 3KB

ensemble2.csv 3KB

README.md 1KB

pred.csv 3KB

rf0_addsex_prediction.csv 3KB

gbm0_fs_prediction.csv 3KB

Tutorial

abhishek_decomp.png 22KB

abhishek_11.png 21KB

abhishek_18.png 19KB

abhishek_2.png 91KB

abhishek_5.png 30KB

abhishek_25.png 54KB

abhishek_21.png 44KB

abhishek_7.png 43KB

abhishek_26.png 20KB

abhishek_13.png 12KB

abhishek_20.png 42KB

abhishek_8.png 49KB

abhishek_1.png 20KB

abhishek_3.png 38KB

abhishek_4.png 31KB

abhishek_10.png 21KB

abhishek_9.png 11KB

abhishek_23.png 26KB

abhishek_22.png 22KB

abhishek_17.png 9KB

Approaching (Almost) Any Machine Learning Problem.ipynb 18KB

abhishek_6.png 74KB

abhishek_15.png 34KB

abhishek_24.png 163KB

abhishek_12.png 44KB

README.md 557B

abhishek_16.png 57KB

abhishek_14.png 7KB

abhishek_19.png 6KB

README.md 419B

# 提交的预测结果文件及说明 benchmark，线上得分0.78469 rf0\_fs\_prediction，随机森林基线模型，前向选择特征，交叉验证0.828276，线上得分0.79426 gbm0\_fs\_prediction，GBM基线模型，前向选择特征，交叉验证0.836179，线上得分0.78469 xgb0\_fs\_prediction，XGBoost基线模型，前向选择特征，交叉验证0.837278，线上得分0.78947 rf0\_rfecv\_addfeature\_prediction，随机森林基线模型，后向选择特征，交叉验证0.82271，线上得分0.77033 rf0\_prediction，随机森林基线模型，手工选择特征，交叉验证0.82606，线上得分0.78947 rf0\_addsex\_prediction，随机森林基线模型，手工选择特征（增加Sex_encode特征），交叉验证0.8238258，线上得分0.79426 gbm0\_prediction，GBM基线模型，手工选择特征，交叉验证0.8305989，线上得分0.77512 xgb0\_prediction，XGBoost基线模型，手工选择特征，交叉验证0.8350366，线上得分0.76555 rf\_tuning\_prediction，随机森林调参模型，手工选择特征，交叉验证0.831672，线上得分0.78947 rf\_tuning\_classweight\_prediction，随机森林调参过采样模型，手工选择特征，交叉验证0.831672，线上得分0.78947 gbm0，线上得分0.78469 pred，线上得分0.78469 ensemble，线上得分0.79426 ensemble1，线上得分0.79904 ensemble2，线上得分0.79426 ensemble3，线上得分0.79904

评论收藏

内容反馈