Model Explainability is The Next Data Science Superpower

Why Model Explainability is The Next Data Science Superpower
Model explainability techniques show you what your model is learning, and seeing inside your model is even more useful than most people expect.I’ve interviewed many data scientists in the last 10 years, and model explainability techniques are my favorite topic to distinguish the very best data scientists from the average. For any single prediction from a model, how did each feature in the data affect that particular prediction
What interactions between features have the biggest effects on a model’s predictions

Answering these questions is more broadly useful than many people realize. The five most important applications of model insights are
Debugging
Informing feature engineering
Directing future data collection
Informing human decision-making
Building Trust

Debugging
The world has a lot of unreliable, disorganized and generally dirty data.

Source: towardsdatascience.com