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Open-Source Drift Detection Tools in Action: Insights from Two Use Cases
Data drifts pose a critical challenge in the lifecycle of machine learning (ML) models, affecting their performance and reliability.
Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction
Leveraging SHAP clustering, our method provides interpretable explanations for model predictions and uses these insights to adaptively refine the model, balancing model complexity with predictive performance.
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