24 lines
1.5 KiB
Markdown
24 lines
1.5 KiB
Markdown
# Explanation-Aware Automated Machine Learning
|
||
|
||
This repository accompanies the research paper:
|
||
|
||
**“Multi-Objective Automated Machine Learning for Explainable Artificial Intelligence: Optimizing Predictive Accuracy and Shapley-Based Feature Stability.”**
|
||
|
||
In high-stakes domains such as agriculture, machine learning models must be not only accurate but also transparent and aligned with domain knowledge. This project presents a novel **multi-objective optimization framework** that jointly maximizes predictive performance and explanation stability. Specifically, we introduce a formal metric based on the **variance of Shapley Additive Explanations across cross-validation folds**, embedding it directly into the model selection process.
|
||
|
||
Our approach leverages the **Non-dominated Sorting Genetic Algorithm II** to evolve models that balance predictive accuracy with robust, semantically consistent explanations. When applied to potato yield prediction, the framework outperforms both **H2O.ai's Automatic Machine Learning platform** and traditional grid search, producing models that are both high-performing and interpretable.
|
||
|
||
---
|
||
|
||
## 🔍 Key Features
|
||
|
||
- Multi-objective optimization for predictive accuracy and explanation stability
|
||
- Shapley-based metric embedded into the model selection loop
|
||
- Implementation using NSGA-II for evolutionary search
|
||
- Reproducible case study in potato yield forecasting
|
||
- Baseline comparisons with grid search and H2O.ai’s platform
|
||
|
||
---
|
||
|
||
## 📂 Repository Structure
|