Imputation Model

Imputation models aim to estimate missing values in datasets, a crucial preprocessing step for many machine learning applications. Current research focuses on improving imputation accuracy and efficiency using diverse techniques, including neural networks (like radial basis function and recurrent neural networks), random forests, and ensemble methods that dynamically weight different imputation-prediction pipelines. These advancements are significant because accurate imputation enhances the reliability and performance of downstream analyses across various fields, from healthcare and recommendation systems to industrial equipment monitoring.