Guidotti R.; Ruggieri S.
CNR-ISTI, Pisa, Italy; Università di Pisa, Pisa, Italy and CNR-ISTI, Pisa, Italy
Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.
Interpretable models, Stability, Overfitting
Ruggieri Salvatore, Guidotti Riccardo
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 397161
Year: 2018
Type: Rapporto tecnico
Creation: 2019-01-02 10:18:21.000
Last update: 2020-12-18 19:36:13.000
CNR authors
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:397161