Iterative Imputation in Machine Learning: A Comprehensive Guide
Handling missing data is a crucial step in the machine learning workflow. While simple imputation techniques like mean, median, or mode imputation are widely used, they often fail to capture complex relationships within the data. Iterative Imputation is an advanced method that fills in missing values by leveraging the interdependence among features, making it a powerful alternative for datasets with intricate patterns. Table of Contents What is Iterative Imputation? Why Use Iterative Imputation? How Iterative Imputation Works Advantages and Limitations of Iterative Imputation Iterative Imputation in Python: Step-by-Step Guide Best Practices for Using Iterative Imputation Conclusion 1. What is Iterative Imputation? Iterative Imputation is a technique where each feature with missing values is modeled as a function of the other features in the dataset. Missing values are predicted iteratively using regression models until the imputed values stabilize. This process not only fills in...