[Home Credit Default Risk] Tuning Automated Feature Engineering (Exploratory)
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-tuning-automated-feature-engineering”
Tag
- Home Credit Default Risk 5
- Kaggle 4
- Porto Seguro 4
- subword 4
- Titanic 3
- attention 3
- transformer 3
- Feedback Prize - English Language Learning 3
- SentencePiece 2
- boruta 2
- feature selection 2
- SHAP 2
- TabNet 2
- python 1
- blog 1
- jekyll 1
- K-fold cross validation 1
- KNN 1
- SVM 1
- DecisionTree 1
- NaiveBayes 1
- 앙상블 기법 1
- 비지도 학습 1
- AdaBoost 1
- ExtraTrees 1
- GradientBoosing 1
- LDA 1
- XGBoost 1
- Encoding 1
- LightGBM 1
- Polynomial 1
- interaction terms 1
- Bi LSTM-CRF Model 1
- BPE 1
- NMT 1
- tensorflow 1
- WordPiece 1
- GNMT 1
- unigram language model 1
- subword regularization 1
- BLEU 1
- deep feature synthesis 1
- featuretools 1
- Deep Feature Synthesis 1
- SMOTE 1
- class imbalance 1
- class weights 1
- boruta SHAP 1
- lazypredict 1
- synthetic data 1
- ydata_synthetic 1
- CWGAN-GP 1
- chatbot 1
- LCE 1
- Local Cascade Ensemble 1
- 한번더 1
- ParShap 1
- BERT 1
- Optuna 1
- minituna 1
- ML pipeline 1
- scikit-learn 1
- Sentence-BERT 1
- SBERT 1
- RoBERTa 1
- DeBERTa 1
Home Credit Default Risk
[Home Credit Default Risk] Automated Feature Engineering Basics
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-automated-feature-engineering-basics”
[Home Credit Default Risk] Introduction: Manual Feature Engineering (part two)
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-manual-feature-engineering-pt2”
[Home Credit Default Risk] Introduction to Manual Feature Engineering
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-introduction-manual-feature-engineering”
[Home Credit Default Risk] Start Here: A Gentle Introduction
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-start-here-a-gentle-introduction”
Kaggle
[Porto Seguro] Porto Seguro Exploratory Analysis and Prediction
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-exploratory-analysis-and-prediction”
[Titanic] Introduction to Ensembling Stacking
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-introduction-to-ensembling-stacking”
[Titanic] Top 4% with ensemble modeling
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-titanic-top-4-with-ensemble-modeling”
[Titanic] EDA To Prediction(DieTanic)
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-eda-to-prediction”
Porto Seguro
[Porto Seguro] Porto Seguro Exploratory Analysis and Prediction
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-exploratory-analysis-and-prediction”
[Porto Seguro] XGBoost_CV(LB.284)
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-xgboost-cv-lb-284”
[Porto Seguro] Interactive Porto Insights - A Plot.ly Tutorial
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-interactive-porto-insights-plot-ly”
[Porto Seguro] Data Preparation Exploration
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-data-preparation-exploration”
subword
[논문] Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018)
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018)
[논문] Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)
[논문] SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (2018)
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (2018)
[논문] Neural Machine Translation of Rare Words with Subword Units (2016)
Neural Machine Translation of Rare Words with Subword Units (2016)
Titanic
[Titanic] Introduction to Ensembling Stacking
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-introduction-to-ensembling-stacking”
[Titanic] Top 4% with ensemble modeling
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-titanic-top-4-with-ensemble-modeling”
[Titanic] EDA To Prediction(DieTanic)
[공지사항] “출처: https://www.kaggle.com/code/jundthird/kor-eda-to-prediction”
attention
트랜스포머(Transformer) 설명
[공지사항] “출처: https://wikidocs.net/31379”
논문에서 설명하지 않은 Transformer Details
[공지사항] “출처: https://tunz.kr/post/4”
[논문] NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE (2016)
NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE (2016)
transformer
트랜스포머를 이용한 한국어 챗봇(Transformer Chatbot Tutorial)
[공지사항] “출처: https://wikidocs.net/89786”
트랜스포머(Transformer) 설명
[공지사항] “출처: https://wikidocs.net/31379”
논문에서 설명하지 않은 Transformer Details
[공지사항] “출처: https://tunz.kr/post/4”
Feedback Prize - English Language Learning
[Feedback Prize - English Language Learning] Speeding up Transformer w/ Optimization Strategies
Introduction state-of-the-art 또는 SOTA model을 훈련시키기 위해서는 GPU가 절대적으로 필요합니다. 그리고 Google Colab이나 Kaggle에서 사용할 수 있다고 해도 메모리 제약 문제가 여전히 발생합니다.
[Feedback Prize - English Language Learning] SWA, Apex AMP & Interpreting Transformers in Torch
SWA-LP & Interpreting Transformer Interactively
[Feedback Prize - English Language Learning] DeBERTa LayerwiseLR LastLayerReinit in TensorFlow
DeBERTa LLRD + LastLayerReinit with TensorFlow MultilabelStratifiedKFold split of the data
SentencePiece
[tensorflow] 센텐스피스(SentencePiece)
[공지사항] “출처: https://wikidocs.net/86657”
[논문] SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (2018)
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (2018)
boruta
Boruta SHAP: 모든 data scientist가 알아야 할 feature selection을 위한 도구
출처: https://towardsdatascience.com/boruta-shap-an-amazing-tool-for-feature-selection-every-data-scientist-should-know-33a5f01285c0
Feature selection 알고리즘인 Boruta algorithm에 대한 설명
출처: https://towardsdatascience.com/boruta-explained-the-way-i-wish-someone-explained-it-to-me-4489d70e154a
feature selection
Boruta SHAP: 모든 data scientist가 알아야 할 feature selection을 위한 도구
출처: https://towardsdatascience.com/boruta-shap-an-amazing-tool-for-feature-selection-every-data-scientist-should-know-33a5f01285c0
Feature selection 알고리즘인 Boruta algorithm에 대한 설명
출처: https://towardsdatascience.com/boruta-explained-the-way-i-wish-someone-explained-it-to-me-4489d70e154a
SHAP
Boruta SHAP: 모든 data scientist가 알아야 할 feature selection을 위한 도구
출처: https://towardsdatascience.com/boruta-shap-an-amazing-tool-for-feature-selection-every-data-scientist-should-know-33a5f01285c0
SHAP 값을 사용하여 machine learning 모델 작동 방식 설명
출처: https://towardsdatascience.com/using-shap-values-to-explain-how-your-machine-learning-model-works-732b3f40e137
TabNet
Imbalanced Data를 다루기 위해 SMOTE 그만 사용하기
[공지사항] “출처: https://syslog.ravelin.com/classification-with-tabnet-deep-dive-49a0dcc8f7e8”
TabNet으로 하는 Classification: Deep Dive
[공지사항] “출처: https://syslog.ravelin.com/classification-with-tabnet-deep-dive-49a0dcc8f7e8”
python
첫 포스팅
[공지사항] “첫 공지사항”
blog
첫 포스팅
[공지사항] “첫 공지사항”
jekyll
첫 포스팅
[공지사항] “첫 공지사항”
K-fold cross validation
cross validation, KNN, SVM, DecisionTree, NaiveBayes, 앙상블 기법, 비지도 학습
K-폴드 교차 검증(K-fold cross validation) 학습 데이터의 일정 부분을 검증데이터로 쓰되, n번의 검증 과정을 통해 학습 데이터의 모든 데이터를 한 번씩 검증데이터로 사용하는 방식 Training Set, Validation Set, Test Set의 ...
KNN
cross validation, KNN, SVM, DecisionTree, NaiveBayes, 앙상블 기법, 비지도 학습
K-폴드 교차 검증(K-fold cross validation) 학습 데이터의 일정 부분을 검증데이터로 쓰되, n번의 검증 과정을 통해 학습 데이터의 모든 데이터를 한 번씩 검증데이터로 사용하는 방식 Training Set, Validation Set, Test Set의 ...
SVM
cross validation, KNN, SVM, DecisionTree, NaiveBayes, 앙상블 기법, 비지도 학습
K-폴드 교차 검증(K-fold cross validation) 학습 데이터의 일정 부분을 검증데이터로 쓰되, n번의 검증 과정을 통해 학습 데이터의 모든 데이터를 한 번씩 검증데이터로 사용하는 방식 Training Set, Validation Set, Test Set의 ...
DecisionTree
cross validation, KNN, SVM, DecisionTree, NaiveBayes, 앙상블 기법, 비지도 학습
K-폴드 교차 검증(K-fold cross validation) 학습 데이터의 일정 부분을 검증데이터로 쓰되, n번의 검증 과정을 통해 학습 데이터의 모든 데이터를 한 번씩 검증데이터로 사용하는 방식 Training Set, Validation Set, Test Set의 ...
NaiveBayes
cross validation, KNN, SVM, DecisionTree, NaiveBayes, 앙상블 기법, 비지도 학습
K-폴드 교차 검증(K-fold cross validation) 학습 데이터의 일정 부분을 검증데이터로 쓰되, n번의 검증 과정을 통해 학습 데이터의 모든 데이터를 한 번씩 검증데이터로 사용하는 방식 Training Set, Validation Set, Test Set의 ...
앙상블 기법
cross validation, KNN, SVM, DecisionTree, NaiveBayes, 앙상블 기법, 비지도 학습
K-폴드 교차 검증(K-fold cross validation) 학습 데이터의 일정 부분을 검증데이터로 쓰되, n번의 검증 과정을 통해 학습 데이터의 모든 데이터를 한 번씩 검증데이터로 사용하는 방식 Training Set, Validation Set, Test Set의 ...
비지도 학습
cross validation, KNN, SVM, DecisionTree, NaiveBayes, 앙상블 기법, 비지도 학습
K-폴드 교차 검증(K-fold cross validation) 학습 데이터의 일정 부분을 검증데이터로 쓰되, n번의 검증 과정을 통해 학습 데이터의 모든 데이터를 한 번씩 검증데이터로 사용하는 방식 Training Set, Validation Set, Test Set의 ...
AdaBoost
AdaBoost, ExtraTrees, GradientBoosing, LDA
AdaBoost (Adaptive Boosing)
ExtraTrees
AdaBoost, ExtraTrees, GradientBoosing, LDA
AdaBoost (Adaptive Boosing)
GradientBoosing
AdaBoost, ExtraTrees, GradientBoosing, LDA
AdaBoost (Adaptive Boosing)
LDA
AdaBoost, ExtraTrees, GradientBoosing, LDA
AdaBoost (Adaptive Boosing)
XGBoost
XGBoost란
“출처: https://dacon.io/competitions/open/235698/talkboard/404176”
Encoding
Binary Encoding과 Target-based Encoding
“출처: http://www.saedsayad.com/encoding.htm”
LightGBM
LIGHTGBM 이란? 그리고 PARAMETER 튜닝하기
[공지사항] “출처: https://medium.com/@pushkarmandot/https-medium-com-pushkarmandot-what-is-lightgbm-how-to-implement-it-how-to-fine-tune-the-parameters-60347819b7f...
Polynomial
Interpreting Interactions in Regression
[공지사항] “출처: https://www.theanalysisfactor.com/interpreting-interactions-in-regression/”
interaction terms
Interpreting Interactions in Regression
[공지사항] “출처: https://www.theanalysisfactor.com/interpreting-interactions-in-regression/”
Bi LSTM-CRF Model
[논문] Bidirectional LSTM-CRF Models for Sequence Tagging (2015)
Bidirectional LSTM-CRF Models for Sequence Tagging (2015)
BPE
[논문] Neural Machine Translation of Rare Words with Subword Units (2016)
Neural Machine Translation of Rare Words with Subword Units (2016)
NMT
[논문] Neural Machine Translation of Rare Words with Subword Units (2016)
Neural Machine Translation of Rare Words with Subword Units (2016)
tensorflow
[tensorflow] 센텐스피스(SentencePiece)
[공지사항] “출처: https://wikidocs.net/86657”
WordPiece
[논문] Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)
GNMT
[논문] Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016)
unigram language model
[논문] Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018)
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018)
subword regularization
[논문] Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018)
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (2018)
BLEU
BLEU Score(Bilingual Evaluation Understudy Score)
[공지사항] “출처: https://wikidocs.net/31695”
deep feature synthesis
Automated Feature Engineering in Python
[공지사항] “출처: https://towardsdatascience.com/automated-feature-engineering-in-python-99baf11cc219”
featuretools
Automated Feature Engineering in Python
[공지사항] “출처: https://towardsdatascience.com/automated-feature-engineering-in-python-99baf11cc219”
Deep Feature Synthesis
[논문] Deep Feature Synthesis: Towards Automating Data Science Endeavors (2015)
출처 : https://dm-gatech.github.io/CS8803-Fall2018-DML-Papers/deep-feature-synthesis.pdf
SMOTE
Class imbalance를 다루기 위해 SMOTE 그만 사용하기
Stop Using SMOTE to Treat Class Imbalance
class imbalance
Class imbalance를 다루기 위해 SMOTE 그만 사용하기
Stop Using SMOTE to Treat Class Imbalance
class weights
Class imbalance를 다루기 위해 SMOTE 그만 사용하기
Stop Using SMOTE to Treat Class Imbalance
boruta SHAP
Boruta SHAP: 모든 data scientist가 알아야 할 feature selection을 위한 도구
출처: https://towardsdatascience.com/boruta-shap-an-amazing-tool-for-feature-selection-every-data-scientist-should-know-33a5f01285c0
lazypredict
단 2줄의 코드로 machine learning model 40개 적용하기
[공지사항] “출처: https://medium.com/@fareedkhandev/apply-40-machine-learning-models-in-two-lines-of-code-c01dad24ad99”
synthetic data
Fraud Machine Learning Modelling을 돕기 위한 합성 데이터
[공지사항] “출처: https://towardsdatascience.com/synthetic-data-to-help-fraud-machine-learning-modelling-c28cdf04e12a”
ydata_synthetic
Fraud Machine Learning Modelling을 돕기 위한 합성 데이터
[공지사항] “출처: https://towardsdatascience.com/synthetic-data-to-help-fraud-machine-learning-modelling-c28cdf04e12a”
CWGAN-GP
Fraud Machine Learning Modelling을 돕기 위한 합성 데이터
[공지사항] “출처: https://towardsdatascience.com/synthetic-data-to-help-fraud-machine-learning-modelling-c28cdf04e12a”
chatbot
트랜스포머를 이용한 한국어 챗봇(Transformer Chatbot Tutorial)
[공지사항] “출처: https://wikidocs.net/89786”
LCE
Random Forest 아니면 XGBoost? 이제는 Local Cascade Ensemble (LCE)를 살펴봐야 할 시간입니다
[공지사항] “출처: https://towardsdatascience.com/random-forest-or-xgboost-it-is-time-to-explore-lce-2fed913eafb8”
Local Cascade Ensemble
Random Forest 아니면 XGBoost? 이제는 Local Cascade Ensemble (LCE)를 살펴봐야 할 시간입니다
[공지사항] “출처: https://towardsdatascience.com/random-forest-or-xgboost-it-is-time-to-explore-lce-2fed913eafb8”
한번더
Random Forest 아니면 XGBoost? 이제는 Local Cascade Ensemble (LCE)를 살펴봐야 할 시간입니다
[공지사항] “출처: https://towardsdatascience.com/random-forest-or-xgboost-it-is-time-to-explore-lce-2fed913eafb8”
ParShap
어떤 features가 Overfitting이 되는 걸까요?
[공지사항] “출처: https://towardsdatascience.com/which-of-your-features-are-overfitting-c46d0762e769”
BERT
[논문] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019)
출처: https://arxiv.org/pdf/1810.04805.pdf
Optuna
Hyperparameter Optimization Framework인 Optuna 구현 소개
[공지사항] “출처: https://medium.com/optuna/an-introduction-to-the-implementation-of-optuna-a-hyperparameter-optimization-framework-33995d9ec354”
minituna
Hyperparameter Optimization Framework인 Optuna 구현 소개
[공지사항] “출처: https://medium.com/optuna/an-introduction-to-the-implementation-of-optuna-a-hyperparameter-optimization-framework-33995d9ec354”
ML pipeline
Scikit-learn으로 ML 모델링부터 ML Pipeline까지
[공지사항] “출처: https://towardsdatascience.com/from-ml-model-to-ml-pipeline-9f95c32c6512”
scikit-learn
Scikit-learn으로 ML 모델링부터 ML Pipeline까지
[공지사항] “출처: https://towardsdatascience.com/from-ml-model-to-ml-pipeline-9f95c32c6512”
Sentence-BERT
[논문] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019)
출처: https://arxiv.org/abs/1908.10084
SBERT
[논문] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019)
출처: https://arxiv.org/abs/1908.10084
RoBERTa
[논문] RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019)
출처: https://arxiv.org/abs/1907.11692
DeBERTa
[논문] DeBERTa: Decoding-enhanced BERT with Disentangled Attention (2021)
출처: https://arxiv.org/abs/2006.03654