For categorical inputs, what should missing values be replaced with?

When handling missing values for categorical inputs, replacing missing entries with a categorical value is appropriate because it maintains the integrity and nature of the data. Categorical variables represent distinct groups or categories, and by substituting missing values with a suitable category, you ensure that the final dataset continues to reflect the original characteristics of the variable. This method can involve placing the missing values into a designated "missing" category or using the mode (most frequent category) if applicable.

Utilizing fixed categories or numerical substitutes like the mean or interval values would distort the categorical nature of the data, as those methods are better suited for continuous variables. Therefore, defining a proper category for missing values is essential for accurate data representation and effective analysis in modeling processes.