Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
model_pipeline_final.pdf
Search
Maxwell
September 18, 2018
Science
1
210
model_pipeline_final.pdf
model pipeline and others in Home Credit Default Risk competition.
Thanks to team mates.
Maxwell
September 18, 2018
Tweet
Share
More Decks by Maxwell
See All by Maxwell
Causal Impact -paper summary-
hoxomaxwell
3
810
Great Barrier Reef Model Pipeline: 15th place
hoxomaxwell
1
210
Lecture materials at the University of Tokyo School of Medicine
hoxomaxwell
1
140
Kaggle Hungry Geese
hoxomaxwell
1
110
HuBMAP 17th place model pipeline
hoxomaxwell
1
95
LT: Shallow Dive into Bayes Factor
hoxomaxwell
6
1.4k
Kaggle APTOS 2019 @ U-Tokyo Med
hoxomaxwell
1
420
Cornell Birdcall 36th place solution
hoxomaxwell
2
230
Kaggle Bengali.AI 6 th place solution
hoxomaxwell
4
8.5k
Other Decks in Science
See All in Science
高校生就活へのDA導入の提案
shunyanoda
0
1.7k
CV_5_3dVision
hachama
0
140
テンソル分解による糖尿病の組織特異的遺伝子発現の統合解析を用いた関連疾患の予測
tagtag
2
200
07_浮世満理子_アイディア高等学院学院長_一般社団法人全国心理業連合会代表理事_紹介資料.pdf
sip3ristex
0
520
KH Coderチュートリアル(スライド版)
koichih
1
42k
06_浅井雄一郎_株式会社浅井農園代表取締役社長_紹介資料.pdf
sip3ristex
0
540
[第62回 CV勉強会@関東] Long-CLIP: Unlocking the Long-Text Capability of CLIP / kantoCV 62th ECCV 2024
lychee1223
1
960
ド文系だった私が、 KaggleのNCAAコンペでソロ金取れるまで
wakamatsu_takumu
2
760
データマイニング - グラフデータと経路
trycycle
PRO
1
180
データベース09: 実体関連モデル上の一貫性制約
trycycle
PRO
0
810
データマイニング - コミュニティ発見
trycycle
PRO
0
110
3次元点群を利用した植物の葉の自動セグメンテーションについて
kentaitakura
2
1.3k
Featured
See All Featured
A better future with KSS
kneath
238
17k
Put a Button on it: Removing Barriers to Going Fast.
kastner
60
3.9k
The Cost Of JavaScript in 2023
addyosmani
51
8.6k
Reflections from 52 weeks, 52 projects
jeffersonlam
351
21k
The Language of Interfaces
destraynor
158
25k
Keith and Marios Guide to Fast Websites
keithpitt
411
22k
Being A Developer After 40
akosma
90
590k
What's in a price? How to price your products and services
michaelherold
246
12k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
60k
Producing Creativity
orderedlist
PRO
346
40k
VelocityConf: Rendering Performance Case Studies
addyosmani
332
24k
How to train your dragon (web standard)
notwaldorf
96
6.1k
Transcript
ikiri_DS Model PipeLine 600+1 ( LB804 ) FEATURES 1000+1 (
LB803 ) meta app meta bur Kernel GP Nejumi features Tereka features + LGBM 5 3 tosh 5 + CatBoost 5 2 1 + LGBM * 4 3 1 + CNN 7 Residual 2 + ExtTree 4 3 1 Residual 1 ( corrected with residual regression ) Blending CV 0.8094 Adversarial Stochastic Blending CV 0.8096 Adversarial Stochastic Blending CV 0.81050 * model drawn in next page + NN 1 3 ONODERA Maxwell Nejumi Tereka RK 1 2 3 4 5 6 7 Branden features 8 Branden + NN 1 3 takuoko features 9 Angus features 10 takuoko nejumi feature Angus + Res2 + LGBM 1 6 + Res1 + LGBM 1 6 1 or 2 or 5 + LGBM 1 or 2 or 5 + CatBoost or + LGBM 5 1 or 2 5 + LGBM 8 + LGBM 9 + LGBM 10 Adversarial Stochastic Blending CV : 0.8061 29.Aug.2018 Tam Tam features 11 + LGBM 11 + RGF 1 + LGBM 11 + RNN 7 1 * using hidden layer as additional features to correct residuals. + CNN 7 + hidden + Res3 + LGBM 1 6 + RGF 1 + Res2 + LGBM 1 6 + LGBM 5 RK features 12 + LGBM 12 1 or 2 12 + LGBM 8 1 or 2 8 + LGBM 3 1 5 or 3 2 5 + LGBM 8 1 12 or 8 2 12 Public 0.8085 17 th Private 0.8017 18 th + LGBM 8 + LGBM 9 + LGBM 10 Ireko DAE 13 Ireko8 + NN 1 13 + NN 1 + NN 1 13 Nejumi prediction Public 0.8093 10 th Private 0.8016 18 th Public 0.8080 23 th Private 0.8028 14 th + RNN 7 1 Public 0.8110 3 rd Private 0.8042 5 th Giba Post Processing Public 2nd 0.81241 Private 2nd 0.80561 Home Credit Default Risk partial partial partial + LGBM 8 1 or 2 8 or 12 + LGBM 3 1 or 2 3 or 12 3 + LGBM 6 1 Residual 3 + hidden + LGBM 1 6' or 6' 1 + LGBM 6' 2 Blending
ikiri_DS Model PipeLine 600+1 ( LB804 ) FEATURES 1000+1 (
LB803 ) meta app meta bur Kernel GP Nejumi features Tereka features tosh + LGBM * 4 3 1 + CNN 7 Residual 2 Residual 1 ( corrected with residual regression ) Blending CV 0.8085 Adversarial Stochastic Blending CV 0.8085 Adversarial Stochastic Blending CV 0.8097 * model drawn in next page ONODERA Maxwell Nejumi Tereka RK 1 2 3 4 5 6 7 Branden features 8 Branden + NN 1 3 takuoko features 9 Angus features 10 takuoko nejumi feature Angus + Res2 + LGBM 1 6 + Res1 + LGBM 1 6 + LGBM 8 + LGBM 9 + LGBM 10 Adversarial Stochastic Blending CV : 0.8061 29.Aug.2018 Tam Tam features 11 + LGBM 11 + LGBM 11 + RNN 7 1 * using hidden layer as additional features to correct residuals. + CNN 7 + hidden + Res3 + LGBM 1 6 + RGF 1 + Res2 + LGBM 1 6 + LGBM 5 RK features 12 + LGBM 12 1 or 2 12 + LGBM 8 1 or 2 8 Public 0.8071 26 th Private 0.8009 37 th + LGBM 8 + LGBM 9 + LGBM 10 Ireko DAE 13 Ireko8 + NN 1 13 + NN 1 + NN 1 13 Nejumi prediction Public 0.8082 23 th Private 0.8022 18 th Public 0.8080 23 th Private 0.8028 14 th Public 0.8099 7 th Private 0.8040 6 th Giba Post Processing Home Credit Default Risk partial + LGBM 8 1 12 or 8 2 12 partial 1 or 2 + LGBM + LGBM 6 1 Residual 3 + hidden + LGBM 1 6' or 6' 1 + LGBM 6' 2 Blending + ExtTree 4 3 1 + NN 1 3 + RGF 1 + LGBM 4 3 2 + XGB 4 3 1 + NN 1 + RNN 7 1 + hidden + Res3 + LGBM 1 6 + Res1 + LGBM 1 6 + hidden + Res4 + LGBM 1 6 stacking with LGBM CV 0.8080 Public 0.8070 / Private 0.8015 Stacking prediction Stacking + LGBM 3 1 or 2 3
application bureau bureau balance AUC : 0.683 (SEED71) 0.683 (SEEDs
avg) AUC 0.772 (SEED71) 0.773 (SEEDs avg) XGBoost app meta feature XGBoost prev meta feature 229 features 300 features all data stacking-like Light GBM 5 stratified fold ( shuffle = True ) 5 / 8 SEEDs rank averaged SEED : 71 for model fit SEED : 710, 711, 712, 713, 714 ( 715, 716, 717 ) for OOF prediction hyper parameter tuned for 603 features (reflected on meta features) XGBoost bureau meta feature ONODERA BASIC FEATURES 600 features NEJUMI FEATURES ( interest rate ) 1 feature 603 ( 604 ) features Local CV 0.80641 Public LB / Private LB 0.80569 / 0.79853 100 th / 105 th AUC 0.710 (SEED71) 0.712 (SEEDs avg) previous inst POS_CASH credit 952 features Local CV 0.80646 LB 0.804 ( ~ 0.805 ) Maxwell 603 ( 604 ) selected features based on ONODERA criteria w/o feature selection Stacking-like Light GBM