Evaluating Classification Feasibility Using Functional Dependencies
| Autor: | Vasile-Marian Scuturici, Jean-Marc Petit, Marie Le Guilly |
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| Rok vydání: | 2020 |
| Předmět: |
Black box (phreaking)
Source lines of code Computer science business.industry Magic (programming) 0102 computer and information sciences 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Domain (software engineering) Statistical classification 010201 computation theory & mathematics 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Functional dependency computer Interpretability Abstraction (linguistics) |
| Zdroj: | Transactions on Large-Scale Data-and Knowledge-Centered Systems XLIV ISBN: 9783662622704 |
| DOI: | 10.1007/978-3-662-62271-1_5 |
| Popis: | With the vast amount of available tools and libraries for data science, it has never been easier to make use of classification algorithms: a few lines of code are enough to apply dozens of algorithms on any dataset. It is therefore “super easy” for data scientists to produce machine learning (ML) models in a very limited time. On the counterpart, domain experts may have the impression that such ML models are just a black box, almost magic, that would work on any dataset without really understanding why. For this reason, related to interpretability of machine learning, there is an urgent need to reconcile domain experts with ML models and to identify at the right level of abstraction, techniques to get them implied in the ML model construction. |
| Databáze: | OpenAIRE |
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