Alessandro Sabatino

Sergio Caprioli, Jacopo Foschi, Riccardo Crupi et al.

Environmental, Social, and Governance (ESG) datasets are frequently plagued by significant data gaps, leading to inconsistencies in ESG ratings due to varying imputation methods. This paper explores the application of established machine learning techniques for imputing missing data in a real-world ESG dataset, emphasizing the quantification of uncertainty through prediction intervals. By employing multiple imputation strategies, this study assesses the robustness of imputation methods and quantifies the uncertainty associated with missing data. The findings highlight the importance of probabilistic machine learning models in providing better understanding of ESG scores, thereby addressing the inherent risks of wrong ratings due to incomplete data. This approach improves imputation practices to enhance the reliability of ESG ratings.

Riccardo Crupi, Daniele Regoli, Alessandro Damiano Sabatino et al.

Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains. Malfunctions, frauds, threats, in addition to being correctly identified, oftentimes need a valid explanation in order to effectively perform actionable counteracts. The ever more widespread use of sophisticated Machine Learning approach to identify anomalies make such explanations more challenging. We present the Decision Tree Outlier Regressor (DTOR), a technique for producing rule-based explanations for individual data points by estimating anomaly scores generated by an anomaly detection model. This is accomplished by first applying a Decision Tree Regressor, which computes the estimation score, and then extracting the relative path associated with the data point score. Our results demonstrate the robustness of DTOR even in datasets with a large number of features. Additionally, in contrast to other rule-based approaches, the generated rules are consistently satisfied by the points to be explained. Furthermore, our evaluation metrics indicate comparable performance to Anchors in outlier explanation tasks, with reduced execution time.