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20202024
most citedStatistical stability indices for LIME: obtaining reliable explanations for Machine Learning models

227 citations · 320 across the 7 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

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…

cs.LG2020★ 6 cited

PSD2 Explainable AI Model for Credit Scoring

Neus Llop Torrent, Giorgio Visani, Enrico Bagli

The aim of this project is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models, preserving at the same time the model interpret…

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…