What strategy does the Imputer use to fill in missing values in data?

The Imputer is a data preprocessing technique used to handle missing values in datasets. A common strategy used by the Imputer is to fill in missing values with the median value of the available data for that feature.

Using the median to fill in missing values is particularly advantageous in cases where the dataset might contain outliers, as the median is less sensitive to extreme values than the mean. This helps to maintain the overall distribution of the data and ensures that the imputed values do not distort the analysis that follows. By selecting the median value, the Imputer provides a robust approach to handling missing data while preserving the integrity of the dataset's overall statistical properties.

In contrast, randomly replacing values or using zeroes could introduce additional noise or bias, which may complicate the predictive modeling process or lead to misleading interpretations. Using the mean value can also be problematic when the dataset contains significant outliers, as it can skew the average and lead to less accurate results. Thus, the use of the median is a well-regarded choice in data imputation strategies.