Explainable AutoML Pipeline

An explainable AutoML pipeline automatically searches over preprocessing steps, model types and hyperparameters using cross-validation, then explains the chosen model with interpretation tools such as SHAP values and partial dependence.

설명 가능한 AutoML 파이프라인은 교차검증으로 전처리 단계, 모형 유형, 하이퍼파라미터를 자동 탐색한 뒤 SHAP 값과 부분의존도 같은 해석 도구로 선택된 모형을 설명하는 방법이다.

How do I automatically find a good predictive model and still explain what drives its predictions?

좋은 예측모형을 자동으로 찾으면서도 예측을 이끄는 요인을 어떻게 설명할 수 있나요?

An explainable AutoML pipeline compares many candidate models and settings under cross-validation and selects the best performer, then applies model-agnostic explanations to show which features drive predictions. It fits tabular prediction tasks where you want a strong benchmark quickly but must still report interpretable results.

When to use Explainable AutoML Pipeline

• Tabular classification or regression with many possible model choices
• Building a benchmark before hand-tuning a specific model
• Reporting both predictive performance and feature effects to non-technical readers

When to use something else

• The goal is estimating causal effects of a treatment: use double machine learning
• Inference on specific coefficients is required: use a prespecified regression model

Assumptions and requirements

• Enough labeled data for cross-validation
• A held-out test set not used during the search
• A predefined evaluation metric suited to the task
• No outcome leakage in features

What the analysis reports

• Leaderboard of candidate models with cross-validated scores
• Selected pipeline and hyperparameters
• Test-set performance
• SHAP summary plot or permutation importance
• Partial dependence plots

How to report it

The AutoML search selected a [model type] with a cross-validated AUC of [value] (SD [value]), and SHAP values indicated that [feature] contributed most to predictions.

Related methods

• XGBoost
• Random Forest
• SHAP Analysis

Run Explainable AutoML Pipeline on Quorant