A Métrica CFIS: Uma Nova Perspectiva na Análise da Importância das Featuresem Modelos de Machine Learning
Year: 2024
Type: article
Abstract: The growing demand for machine learning models that deliver quick and accurate responses drives the development of new techniques to enhance model performance and interpretability. In this context, this paper proposes the CFIS (Combined Feature Importance Score), an innovative metric and approach to evaluate featureimportance in machine learning problems. CFIS combines different methods, such as Permutation Importance, regression model coefficients, and correlation with the target variable, to provide a comprehensive view of featurerelevance. By integrating these metrics, CFIS aims to overcome the limitations of individual approaches, offering a more robust and detailed analysis of how each featurecontributes to model performance. The application of CFIS can benefit various fields, enabling machine learning models to be more transparent and effective in their predictions.
Source: Revista Eletrônica e-Fatec
Author: Godoy Marques, Vinicius
Institutions: São Paulo State Technological College, Fatec Garça
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W6949413463
FWCI:
Cites:
Cited by:
Related to: