Explainability techniques implementation in Artificial Intelligence projects

As the demand for Explainable Artificial Intelligence (XAI) continues to grow, understanding techniques for achieving transparency is crucial. This educational nugget explores two key methods: LIME implementation on a Deep Neural Network (DNN) and SHAP implementation on a Support Vector Machine (SVM). By delving into these techniques, the student can gain insights into how to enhance the transparency of AI models, making them more understandable and interpretable.