Interpretability mechanisms helping users in better understanding machine learning models are crucial for Artificial Intelligence acceptance. In this manuscript, our experience in interpretation of random forest regression via surrogate models, i.e. models trying to replicate in an interpretable framework an original fitting difficult to understand, is reported. It is shown how, beyond classical R2 analysis, adequacy of surrogate models can be assessed via variable importance analysis.
Using Surrogate Models and Variable Importance to better Understand Random Forests Regression Fitting
migliorati manlio;simonetto anna
2023-01-01
Abstract
Interpretability mechanisms helping users in better understanding machine learning models are crucial for Artificial Intelligence acceptance. In this manuscript, our experience in interpretation of random forest regression via surrogate models, i.e. models trying to replicate in an interpretable framework an original fitting difficult to understand, is reported. It is shown how, beyond classical R2 analysis, adequacy of surrogate models can be assessed via variable importance analysis.File in questo prodotto:
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