NCA-GENM Question 16
Select 2You are tasked with building a generative AI model for a multimodal application that processes both text and image data. During the model development process, you need to ensure that the model performs consistently well across different datasets and input variations. Which step or steps should you prioritize to achieve this goal?
- A
Perform cross-validation to evaluate the model's performance on different data splits.
- B
Compare multiple model architectures to identify the one with the highest training accuracy.
- C
Conduct feature engineering to extract meaningful representations from both text and image data.
- D
Focus solely on increasing the model's complexity to improve generalization.
- E
Use only the dataset with the highest accuracy during testing to finalize the model.
Show answer and explanation
Correct answers: A, C
Explanation
To ensure consistent performance across datasets and input variations, it is essential to prioritize cross-validation for evaluating model generalization and perform feature engineering to extract meaningful representations from the input data. These steps help the model learn effectively while avoiding overfitting or biased evaluations.
- A. Correct.
Cross-validation is a critical step to ensure the model performs consistently across different datasets and is not overfitted to a specific training set.
- B. Incorrect.
While comparing model architectures is important, focusing solely on training accuracy may not lead to a model that generalizes well to unseen data.
- C. Correct.
Feature engineering is essential for multimodal applications as it helps the model extract meaningful information from complex data types like text and images.
- D. Incorrect.
Increasing model complexity does not necessarily improve generalization. It can lead to overfitting if not handled carefully.
- E. Incorrect.
Using only the dataset with the highest testing accuracy can introduce bias and does not ensure the model performs well on diverse input variations.