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A machine learning engineer has developed the following custom model class with preprocessing logic to combine two columns: However, instances of this class are unable to compute predictions. Which set of changes will update the class so predictions can be computed while continuing to apply the preprocessing logic?
Correct Answer: C
The issue is that the predict() method does not apply the same preprocessing as the fit() method. During training, the model uses preprocessed data (via self.preprocess_input()), but during prediction, it directly uses raw input. This mismatch causes prediction errors because the model expects preprocessed input. By updating the predict() method to call self.preprocess_input(model_input.copy()), both training and prediction use consistent feature transformations, allowing predictions to be computed successfully.