most citedStatistical stability indices for LIME: obtaining reliable explanations for Machine Learning models

227 citations · 314 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Enabling Synthetic Data adoption in regulated domains

Giorgio Visani, Giacomo Graffi, Mattia Alfero +3

The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms, bringing forward new challenges. In particular, the se…

cs.LG2020★ 3 cited

Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes

Giorgio Visani, Federico Chesani, Enrico Bagli +2

In the global economy, credit companies play a central role in economic development, through their activity as money lenders. This important task comes with some drawbacks, mainly…

stat.ML2020★ 66 cited

Metrics for Multi-Class Classification: an Overview

Margherita Grandini, Enrico Bagli, Giorgio Visani

Classification tasks in machine learning involving more than two classes are known by the name of "multi-class classification". Performance indicators are very useful when the aim…

cs.LG2020★ 18 cited

OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms

Giorgio Visani, Enrico Bagli, Federico Chesani

Local Interpretable Model-Agnostic Explanations (LIME) is a popular method to perform interpretability of any kind of Machine Learning (ML) model. It explains one ML prediction at…

cs.LG2020★ 227 cited

Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models

Giorgio Visani, Enrico Bagli, Federico Chesani +2

Nowadays we are witnessing a transformation of the business processes towards a more computation driven approach. The ever increasing usage of Machine Learning techniques is the cl…