What is imputation?
The process of replacing missing data with estimated values
imputation explained in plain English
Analogy
Imputation is like filling in the missing pieces of a puzzle. Just as you might use the surrounding pieces to guess the missing one, imputation uses the available data to estimate the missing values.
Example
For example, a company collecting customer data might use imputation to fill in missing values for customer incomes, based on their ages, locations, and other available information.
How is imputation used?
Imputation is commonly used in data preprocessing for machine learning models. It helps to ensure that the model is trained on a complete and consistent dataset, which can improve its accuracy and reliability.
Common misconceptions about imputation
One common misconception is that imputation is only used for numerical data. However, it can also be used for categorical data, such as filling in missing values for categories like 'male' or 'female'.
History
The concept of imputation has been around for decades, but it has become increasingly important in recent years with the rise of big data and machine learning.
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