Answer: (iv) All of the above

According to Han, Kamber & Pei (Data Mining: Concepts and Techniques), classification and prediction methods are evaluated/affected by the following criteria:

1. Accuracy — the ability of the model to correctly predict the class label of new or previously unseen data.
2. Speed — the computation cost of both building the model (training time) and using it (classification time).
3. Robustness — the ability of the model to handle missing values, noise, and outliers, and to make correct predictions even when the data is imperfect.
4. Scalability — performance as the amount of data grows (not listed but part of the same set).
5. Interpretability/comprehensibility — the extent to which the model is understandable to the user.

Since accuracy, speed, and robustness are all standard criteria that affect classification/prediction methods, the correct option is (iv) All of the above.