Impacto de las estrategias de preprocesamiento de datos en la precisión de algoritmos de regresión lineal
DOI:
https://doi.org/10.55204/trc.v6i2.e724Palabras clave:
preprocesamiento de datos, regresión lineal, normalización, imputación, aprendizaje automático, revisión sistemáticaResumen
El preprocesamiento de datos es una etapa determinante en el rendimiento de los modelos de regresión lineal, donde la calidad de los datos, incluyendo el tratamiento de valores faltantes, la detección de outliers, la normalización y la codificación de variables, influyen directamente en la capacidad predictiva de los algoritmos de aprendizaje automático. Esta revisión sistemática, conducida bajo la metodología PRISMA 2020 (Preferred Reporting Items for Systematic reviews and Meta-Analyses), bajo esta misma óptica, se analiza 40 artículos científicos publicados entre 2021 y 2026 en bases de datos reconocidas como Scopus, Web of Science, Springer Nature, MDPI, IEEE y ScienceDirect. Dentro de este contexto, se destaca que los modelos de regresión lineal constituyen una herramienta fundamental en la inteligencia artificial y el análisis de datos, donde la calidad del preprocesamiento influye directamente en la precisión de los resultados obtenidos. En el desarrollo del estudio, se analizaron técnicas clave como la limpieza de datos, la imputación de valores faltantes, la normalización o estandarización y a esto se suma la codificación de variables categóricas. En cuanto a las aplicaciones prácticas y futuras líneas de investigación, los hallazgos permiten su implementación en modelos predictivos relacionados con ventas, estimación de precios e indicadores académicos, como también, plantear la extensión del análisis hacia modelos no lineales y conjuntos de datos de mayor dimensionalidad. No obstante, un adecuado preprocesamiento de datos mejora significativamente la precisión predictiva de los modelos de regresión lineal, contribuyendo a reducir problemas de overfitting y underfitting.Descargas
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