Model Explainability
Explore model explainability methods such as LIME and SHAP to understand how machine learning models make predictions. Learn to interpret feature contributions and visualize impacts, enabling you to trust and communicate model decisions effectively.
We'll cover the following...
Let's start with a vital question. That is, why should we trust our model?
Overview
It's a fact that data science and machine learning have naturally found their way to the biggest business and political stories in a concise timeline. Machine learning or deep learning algorithms built into automation and AI systems lack transparency. Machine learning models are often considered black boxes.
Ironically, this transparency has become more visible and challenging for data scientists to explain and interpret their machine learning models (specifically deep learning neural networks). It isn’t easy (maybe impossible ...