Unveiling the Secrets of Model Interpretability
When it comes to model interpretability, it's easy to get caught up in analyzing feature importance, but simply identifying the 'what' behind a model's decisions is only half the battle. To truly grasp the inner workings of your machine learning model, you need to dig deeper and uncover the 'why' behind its feature importance.
From 'what' to 'why': drilling down into feature relationships
By focusing on the 'why', you can reveal the underlying mechanisms driving your model's decisions. This means identifying the top drivers of predictions and exploring their relationships through causal graph analysis. A causal graph is a visual representation of how variables interact and influence one another, providing valuable insights into the model's decision-making process.
Example: A credit risk assessment model
Suppose you've trained a model to predict the likelihood of a customer defaulting on a loan. The model reveals that ...
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