From 5f1c5fd9824c607bad64543648c0711fc64b9aa7 Mon Sep 17 00:00:00 2001 From: Gracechung-sw Date: Sun, 21 Apr 2024 10:34:27 +0900 Subject: [PATCH] add model evaluation --- README.md | 4 +- assets/img/confusion-matrix.png | Bin 0 -> 56831 bytes assets/img/k-fold.png | Bin 0 -> 81672 bytes .../README.md | 22 ++-- .../evaluation.md | 101 ++++++++++++++++++ 5 files changed, 110 insertions(+), 17 deletions(-) create mode 100644 assets/img/confusion-matrix.png create mode 100644 assets/img/k-fold.png rename {model-validation => model-validation-evaluation-testing}/README.md (71%) create mode 100644 model-validation-evaluation-testing/evaluation.md diff --git a/README.md b/README.md index 73607cf..f531ac7 100644 --- a/README.md +++ b/README.md @@ -49,7 +49,7 @@ 이런 정보들이 함께 저장되어야 추후에 '해당 모델의 성능을 **재현** 할 수 있다.' - Tool: [MLflow](https://mlflow.org/), PyTorch, Pandas, ... -### [Model Validation](./model-validation/README.md) +### [Model Validation/Evaluation/Testing](./model-validation/README.md) - Tool: [Scikit-learn](https://scikit-learn.org/) @@ -145,7 +145,7 @@ Related Engineering: Related Engineering: -- [Model Validation](#model-validation) +- [Model Validation/Evaluation/Testing](#model-validationevaluationtesting) 3-1. 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za3$J`$ou$YnCh)jA0BYpol9T-z}85@_ap>m4R-l*wPw;RT&!%TOMKe>NI1R7NOmkR zJEgn>M{C9w(m}eih$&wSzY1z>N#_m2o|QXYl>6b==6)M)5`n0OjDaNzN0xbc@MFTU zC<(5Q#j{n_KWlCwmc4``loa*9mzNGIw=H>Jc@JBP{S(ycs?(Kg*E+v_GXUyn?L9rW zLT%UeX*IM^kerDODULs@n|Q6L9dtw+FW-;oH~(MW)qm zY5aL?xGv`ORDN%N-`c@J5bT6ld3uUVNJv=u`AK(lc3Rum*zhgP<y z`m<^5)EL`O1qB77k1fp2%94;wkUxG+yuP`GSCwi{MD|Dy>| ZAKa;;3e+5{q9NetrkbW|iLzzLe*lkoA+G=c literal 0 HcmV?d00001 diff --git a/model-validation/README.md b/model-validation-evaluation-testing/README.md similarity index 71% rename from model-validation/README.md rename to model-validation-evaluation-testing/README.md index bc507d6..f17f5a8 100644 --- a/model-validation/README.md +++ b/model-validation-evaluation-testing/README.md @@ -1,8 +1,8 @@ -# Model Validation (모델 평가) +# Model Validation/Evaluation/Testing ## Intro -### 1. Model Validation의 필요성 +### 1. Model Validation/Evaluation/Testing 의 필요성 전통적인 Software 개발시의 테스트를 어떻게 했는지 생각해보자. 내가 백엔드 개발을 할 때는 2가지 방식으로 테스트를 했었다. @@ -14,31 +14,23 @@ 특히, AI 모델의 경우 S/W개발과는 다른 것이 input이 같아도 output(즉, 예측 값)이 조금씩 다를 수 있는데, 이를 어떻게 '테스트' 할 수 있는가??? 그래서 모델 평가는 이것만을 위한 지표가 따로 있다. -아래 블로그에 자세히 정리해 두었다. - -1. [Evaluation1 - 분류 모델 성능 지표 (Accuracy, Confusion Matrix, Precision, Recall, F1 score, ROC AUC )](https://libertegrace.tistory.com/entry/Evaluation1) -2. [Evaluation2. 회귀의 성능 평가 지표(MAE, MSE, RMSE, R제곱)](https://libertegrace.tistory.com/entry/Evaluation2) - -그래서 - -- model validation 지표를 도출해내고 +- Model Validation/Evaluation/Testing 지표를 도출해내고 - 이를 자동화 하는 시스템을 만들어내는 것이 필요해진다. 이를 도와주는 Tool이 대표적으로 -### 모델 평가의 종류 - -1. Model Validation +## Model Validation - 학습 과정의 오류를 조기 진단. - ![img](../assets/img/model-validation.png) - ![img](../assets/img/overfitting-underfitting.png) -2. Model Evaluation +## [Model Evaluation](evaluation.md) - 모델의 출력 분호 및 다양한 매트릭으로 정밀 분석 -3. Behavior Testing +## Behavior Testing + - 서비스 UX 측면의 동작을 반영한 실제 시나리오 모델 결과를 테스트 - [Sklearn 익히기 - train/test data set 분리 및 Cross Validation](https://libertegrace.tistory.com/entry/Sklearn-%EC%9D%B5%ED%9E%88%EA%B8%B0-trainvaltest-data-set-%EB%B6%84%EB%A6%AC-%EB%B0%8F-%EB%8D%B0%EC%9D%B4%ED%84%B0-%EC%A0%84%EC%B2%98%EB%A6%AC) diff --git a/model-validation-evaluation-testing/evaluation.md b/model-validation-evaluation-testing/evaluation.md new file mode 100644 index 0000000..03546aa --- /dev/null +++ b/model-validation-evaluation-testing/evaluation.md @@ -0,0 +1,101 @@ +# Model Evaluation + +## Model Evaluation이란 + +- 머신러닝 또는 딥러닝 모델의 성능을 평가하고 검증하는 과정 +- 모델이 학습 데이터에 대해서만 잘 작동하는지, 아니면 새로운 이전에 본 적 없는 데이터에 대해서도 잘 동작하는지 일반화가 잘 되었는지를 평가 +- 이를 위해 다양한 Evaluation Metrics을 사용하여 모델의 예측 성능을 평가 + +## Model Evaluation 방법 + +- Holdout: 데이터를 Training set와 Test set로 한 번만 분할 +- Cross Validation: 데이터를 여러 부분으로 나누어, 한 부분을 Test Set로 사용하고 나머지 부분으로 모델을 학습시킨다. 이 과정을 각 부분마다 반복한다. + - K-Fold Cross validation + - Stratified K-Fold Cross validation + - Leave-One-Out + - Leave-P-Out + +### 1. Holdout + +정의 + +- 데이터를 Training set와 Test set로 한 번만 분할 +- 일반적으로 데이터의 70~80%는 훈련에 사용하고, 나머지 20~30%는 테스트에 사용 + +특징 + +- 장점 : 간단하고 빠르게 실행 +- 단점: 데이터 셋의 크기가 작은 경우, Test set의 결과가 불안정 할 수 있음, 데이터 분할의 random한 특성으로 결과가 크게 달라질 수 있음 + +### 2. K-Fold + +![img](../assets/img/k-fold.png) + +정의 + +- 데이터를 K개의 부분(Fold)로 나눈 후, 각 Fold를 한 번 씩 Test Set로 사용하고 + 나머지 K-1개 Fold를 훈련 세트로 사용. +- 이 과정을 K번 반복 + +특징 + +- 장점 : 모든 데이터 포인트가 훈련과 테스트에 모두 사용되므로 결과가 더 안정적 +- 단점 : 모델을 K번 학습해야 하므로 계산 비용이 높음 + +### 3. Stratified K-Fold + +정의 + +- K-Fold CV의 변형으로, 각 Fold에서 클래스 비율이 전체 데이터셋의 클래스 비율과 동일하게 유지하도록 유지 + +특징 + +- 장점 : Class Imbalance가 있는 데이터 셋에 효과적 +- 단점 : K-Fold CV와 마찬가지로 계산 비용 높음 + +### 4. Leave-One-Out + +정의 + +- K-Fold의 매우 특수한 경우 +- N개의 **데이터 포인트**가 있을 때, 하나의 데이터 포인트를 Test로 사용하고 나머지 N-1개를 훈련에 사용 +- 이 과정을 N번 반복 + +특징 + +- 장점 : 작은 데이터셋에 효과적 +- 단점 : 매우 높은 계산 비용이 발생. 각 데이터 포인트에 대해 모델을 재학습 + +### 5. Leave-P-Out + +정의 + +- N개의 데이터 포인트 중 P개를 테스트로 사용하고 나머지를 훈련에 사용 P=1이면 LOO와 동일 +- 가능한 모든 조합에 대해 반복 + +특징 + +- 장점 : LOO의 변형으로, P개의 데이터 포인트에 대한 영향을 조사 가능 +- 단점 : 계산 비용이 아주 높음 + +## Model Evaluation에 사용하는 Metrics + +### 1. Confusion Matrix + +![img](../assets/img/confusion-matrix.png) + +- 실제 클래스와 모델이 예측한 클래스 간의 관계를 2x2 테이블로 나타낸 것 +- 분류 문제에서 주로 사용된다 + +### 나머지 + +- [Evaluation1 - 분류 모델 성능 지표 (Accuracy, Confusion Matrix, Precision, Recall, F1 score, ROC AUC )](https://libertegrace.tistory.com/entry/Evaluation1) +- [Evaluation2. 회귀의 성능 평가 지표(MAE, MSE, RMSE, R제곱)](https://libertegrace.tistory.com/entry/Evaluation2) + +- Accuracy +- Precision +- Recall +- F1-Score +- MSE +- MAE +- R-squared