Cara Jago Sertifikasi BNSP Data Science: Bedah Transparansi Model dengan Explainable AI (XAI)

📰 Medium · Data Science

Learn to build transparent models using Explainable AI (XAI) to improve model interpretability and trustworthiness

intermediate Published 13 Apr 2026

Action Steps

Apply SHAP (SHapley Additive exPlanations) to assign feature importance
Use LIME to generate local explanations for model predictions
Implement model interpretability techniques to improve model transparency
Evaluate model performance using metrics such as accuracy and F1-score
Visualize model results using techniques such as partial dependence plots and feature importance plots

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this knowledge to create more transparent and accountable models

Key Insight

💡 Explainable AI (XAI) is crucial for building transparent and trustworthy models