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기계학습모델의 사후해석을 위한 모델 불특정 방법론 적용 사례 연구 - 지하도로 네트워크의 교통상황 판단 데이터 중심으로 KCI 등재
A Case Study on the Application of Model-agnostic Methods for the Post hoc Interpretation of A Machine Learning Model : Focusing on Traffic Situation in a Underground Road Network
pp.83-95
PURPOSES : In this study, model-agnostic methods are applied for interpreting machine learning models, such as the feature global effect, the importance of a feature, the joint effects of features, and explaining individual predictions.
METHODS : Model-agnostic global interpretation techniques, such as partial dependence plot (PDP), accumulated local effect (ALE), feature interaction (H-statistics), and permutation feature importance, were applied to describe the average behavior of a machine learning model. Moreover, local model-agnostic interpretation methods, individual conditional expectation curves (ICE), local surrogate models (LIME), and Shapley values were used to explain individual predictions.
RESULTS : As global interpretations, PDP and ALE-Plot demonstrated the relationship between a feature and the prediction of a machine learning model, where the feature interaction estimated whether one feature depended on the other feature, and the permutation feature importance measured the importance of a feature. For local interpretations, ICE exhibited how changing a feature changes the interested instance’s prediction, LIME explained the relationship between a feature and the instance’s prediction by replacing the machine model with a locally interpretable model, and Shapley values presented how to fairly contribute to the instance’s prediction among the features.
CONCLUSIONS : Model-agnostic methods contribute to understanding the general relationship between features and a prediction or debut a model from the global and/or local perspective, securing the reliability of the learning model.
1. 서론
2. 설명 가능한 기계학습 방법론
2.1. 모델특정기반 방법(Model-specific Methods)
2.2. 전체 데이터기반 기계학습모델불특정 방법(Global Model-agnostic Methods)
2.3. 개별 데이터기반 기계학습모델불특정 방법(Local Model-agnostic Methods)
3. 기계학습모델 설명력 방법론 적용 사례
3.1. 데이터
3.2. 기계학습모델 설명력 방법론 적용 및 해석
4. 결론
REFERENCES
