Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2023

P. MishraExplainable AI Recipes https://doi.org/10.1007/978-1-4842-9029-3_4

4. Explainability for Ensemble Supervised Models

Pradeepta Mishra¹  

(1)

Bangalore, Karnataka, India

 

Ensemble models are considered to be effective when individual models are failing to balance bias and variance for a training dataset. The predictions are aggregated in ensemble models to generate the final models. In the case of supervised regression models, many models are generated, and the averages of all the predictions are taken into consideration to generate the final prediction. Similarly, for supervised classification problems, multiple models are being trained, and each model ...