Exploring explainable AI techniques
In one of the previous recipes, we looked into feature importance as one of the means of getting a better understanding of how the models work under the hood. While this might be quite a simple task in the case of linear regression, it gets increasingly difficult with the complexity of the models.
One of the big trends in the ML/DL field is explainable AI (XAI). It refers to various techniques that allow us to better understand the predictions of black box models. While the current XAI approaches will not turn a black box model into a fully interpretable one (or a white box), they will definitely help us better understand why the model returns certain predictions for a given set of features.
Some of the benefits of having explainable AI models are as follows:
- Builds trust in the model—if the model’s reasoning (via its explanation) matches common sense or the beliefs of human experts, it can strengthen the trust in...
