Comparing Imputation Techniques
Mean/Zero vs. kNN Imputation
In data analysis and machine learning, the issue of missing data in datasets is a challenge that can significantly hinder the accuracy and reliability of statistical models and insights drawn from the data. Missing values arise from a multitude of sources — ranging from errors during data collection to intentional omission where data is not applicable. Regardless of the cause, the impact of missing data cannot be underestimated, as it can lead to biased estimates, reduced statistical power, and ultimately, decisions that are not informed by the full picture.
The crux of addressing missing data lies in the selection of an appropriate imputation technique. Imputation is the process of replacing missing data with substituted values, a critical step to ensure that datasets are complete and ready for analysis or modeling. The choice of imputation method is crucial; it must align with the nature of the data and the pattern of missingness to avoid introducing bias or distortion in the analysis.
Among the plethora of imputation techniques available, mean/zero imputation and k-Nearest Neighbors (kNN) imputation are commonly employed strategies, each with its own merits and considerations. Mean imputation involves replacing missing values with the mean of the available values in the same variable…
