For interval inputs, what is the recommended way to replace any missing values?

Replacing missing values for interval inputs is a critical step in data preprocessing during data analysis or modeling. The most recommended approach for handling missing values in interval data is to replace them with the mean of the available data. This is because the mean takes into account the average of the entire dataset, making it a balanced measure that helps to maintain the overall distribution of the data.

Using the mean preserves the statistical properties of the interval data, allowing for a more accurate representation of the dataset when performing analyses or building models. This method is particularly appropriate when the data is symmetrically distributed without significant outliers, as the mean is sensitive to extreme values. In many situations, using the mean can lead to better predictive performance when fitting models since it keeps the data centered.

While the median might also be a valid choice, particularly in the presence of outliers, it does not utilize all available data in the same way that the mean does. The mode is more suitable for categorical data rather than interval data and is not as informative for continuous numerical inputs. Replacing missing values with a fixed number could introduce bias, as it does not take into consideration the underlying structure of the dataset. Hence, using the mean offers a more robust solution for handling missing values in interval inputs