Selecting robust features for machine-learning applications using multidata causal discovery

Selecting robust features for machine-learning applications using multidata causal discovery

Robust feature selection is vital for creating reliable and interpretable machine-learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigat...

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Bibliographic Details
Main Authors: Saranya Ganesh S., Tom Beucler, Frederick Iat-Hin Tam, Milton S. Gomez, Jakob Runge, Andreas Gerhardus
Format: Article
Language:English
Published: Cambridge University Press 2023-01-01
Series:Environmental Data Science
Subjects:
causal feature selection
machine learning
multivariate time series analysis
tropical cyclones
Online Access:https://www.cambridge.org/core/product/identifier/S2634460223000213/type/journal_article

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https://www.cambridge.org/core/product/identifier/S2634460223000213/type/journal_article

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