Imputation Strategy

Imputation strategies aim to fill in missing data points in datasets, a crucial step for many machine learning applications where complete data is essential. Current research focuses on developing and comparing various imputation methods, including simple techniques like mean/mode imputation and more sophisticated approaches using machine learning models such as transformers, Bayesian Ridge regression, and decision trees, often tailored to specific data types (e.g., temporal, tabular, multimodal). The effectiveness of these strategies is evaluated based on metrics like mean squared error and KL divergence, with a growing emphasis on ensuring fairness and mitigating biases introduced by the imputation process itself. Improved imputation techniques are vital for enhancing the reliability and validity of analyses across diverse fields, from healthcare and environmental science to search engine optimization.