Data imputation

Data imputation is a statistical analysis method that helps fill in missing data points. This technique is used to replace missing data with statistically estimable values that retain the accuracy and completeness of datasets. While data imputation can be useful, it also introduces the risk of introducing bias to a dataset due to the use of a small subset of data to estimate a larger population.

Data imputation is used mainly in the context of predictive analytics, where the availability of data can be limited or incomplete. Imputation enables accurate predictions in a predictive analytics workflow. Without imputation, an analysis of a dataset with missing values would likely result in inaccurate predictions due to the ‘holes’ in the dataset.

Data imputation is not always ideal, though. The values that are inserted to replace the missing data points may produce incorrect or misleading results. As such, it is important to assess the risk of bias before assuming that the imputed data is a valid representation of the original data with which it is replacing.

The most common method used for data imputation is mean substitution. This is the simplest form of imputation, replacing the missing value with the mean of all the other present values. Other methods used for imputation include k-nearest neighbor and multivariate imputation. The complexity of these techniques can vary depending on the size and structure of the dataset in question.

Data imputation is essential for ensuring that datasets are complete when predictive modelling is employed, but it is important to implement this technique responsibly. Datasets need to be thoroughly checked before any decisions are made based on replaced values. Where appropriate, it may be a wise decision to consider alternative methods such as cutting or eliminating a column, or disregarding a prediction entirely if the missing data is too great or the characteristics of the data don’t match the imputation method employed.