Imputation is the process of replacing missing data with substituted values to maintain the integrity of a dataset. This technique is crucial for ensuring that data analyses are accurate and reliable, especially since missing values can lead to biased results or loss of information. By employing imputation, practitioners can enhance the quality of their datasets, allowing for more robust machine learning models and insights.
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