Résumé :

The area of Meta-learning (MtL) leverages knowledge from problems for which successful Machine Learning (ML) solutions are known to support automated algorithm selection for new problems. But far more meta-knowledge can be extracted by relating data properties to algorithmic performance. This topic remains under-explored compared to using MtL for automated algorithm selection. For instance, one may reveal the competencies and limitations of different ML algorithms and highlight data quality issues worth investigating. By deepening such understanding, we expect to contribute to improving the comprehensibility and reliability of the usage of ML models. We also expect to generate contributions in areas that can directly benefit from data and algorithm understanding, such as data pre-processing. The idea is to guide the solution of the previous tasks using meta-knowledge extracted about the dichotomous relationship between data properties and algorithmic performance.