What is meant by mean imputation for missing data? Why is it bad?
Mean imputation is a rarely used practice where null values in a dataset are replaced directly with the corresponding mean of the data.
It is considered a bad practice as it completely removes the accountability for feature correlation. This also means that the data will have low variance and increased bias, adding to the dip in the accuracy of the model, alongside narrower confidence intervals.
