Ensemble Methods for Machine Learning

9 Explaining your ensembles

This chapter covers

• Understanding glass-box versus black-box and global versus local interpretability
• Using global black-box methods to understand pretrained ensemble behavior
• Using local black-box methods to explain pretrained ensemble predictions
• Training and using explainable global and local glass-box ensembles from scratch

When training and deploying models, we’re usually concerned about what the model prediction is. Equally important, however, is why the model made the prediction that it did. Understanding a model’s predictions is a critical component of building robust machine-learning pipelines. This is especially true when machine-learning models are used in high-stakes applications such as in health care ...