Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

Air quality is relevant to society because it poses environmental risks to humans and nature. We use explainable machine learning in air quality research by analyzing model predictions in relation to the underlying training data. The data originate from worldwide ozone observations, paired with geos...

Descrizione completa

Dettagli Bibliografici

Autori principali:	Scarlet Stadtler, Clara Betancourt, Ribana Roscher
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2022-02-01
Serie:	Machine Learning and Knowledge Extraction
Soggetti:	explainable machine learning air quality k-nearest neighbors neural network random forest
Accesso online:	https://www.mdpi.com/2504-4990/4/1/8
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!