Value imputation — это процесс замены отсутствующих значений данных приемлемыми substitutes. Substitute может быть получен из statistics, rules, neighboring values или model, позволяя неполным examples оставаться пригодными для использования.
The process of replacing a missing value with an acceptable substitute. When a value is missing, you can either discard the entire example or you can use value imputation to salvage the example. For example, consider a dataset containing a feature that is supposed to be recorded every hour. However, the temperature reading was unavailable for a particular hour. Here is a section of the dataset: Temperature | ---| 10 | 12 | missing | 20 | 21 | 21 | A system could either delete the missing example or impute the missing temperature as 12, 16, 18, or 20, depending on the imputation algorithm.