The Imputer class is the abstract base class that defines the common interface for all imputation models in microimpute. Every model inherits from it and implements the required abstract methods for fitting and prediction.
Key features¶
All models share standardized fit() and predict() methods, so they can be used interchangeably regardless of underlying implementation. All imputers also share functionality like weighted data handling through a weight_col parameter.
The design enforces that predict() cannot be called before fit(). The base implementation also handles parameter and input data validation, so individual models don’t need to duplicate those checks.
When using imputers in isolation (not through autoimpute), preprocessing is available via preprocess_data, which can normalize the data and split it into train/test sets. See matching