The Importance of ML Imputation Methods
A critical stage in the data preparation pipeline is handling missing data. Imputation errors might result in findings that are skewed or inaccurate. In this blog, we'll contrast dropping data, statistical imputation, and machine learning imputation using the'sklearn' package.
By dropping data, missing value-containing rows or columns are eliminated. The simplest method may not be the most effective because a lot of information might be lost, especially if the dataset is tiny.
Dropping data could mean getting rid of important patient records in a medical dataset. If, for example, the missingness is connected to a particular disease or course of therapy, this could result in biased outcomes.
- Mean Imputation: Substitutes the mean of the missing values for
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