NCA-GENM Question 18
Select 3You are tasked with building a generative AI model that can generate captions for images. During the development process, you notice that the model's performance is inconsistent across different datasets. To improve and validate your model, which of the following techniques should you prioritize?
- A
Perform cross-validation to evaluate the model's performance on different data splits.
- B
Use feature engineering to extract meaningful attributes from the images and their captions.
- C
Randomly add noise to the dataset to test the model's robustness.
- D
Compare multiple models with different architectures to identify the best-performing one.
- E
Skip validation and train the model on the entire dataset to maximize training data usage.
Show answer and explanation
Correct answers: A, B, D
Explanation
To improve and validate the model, it is essential to focus on techniques that enhance the model's ability to generalize and perform well on unseen data. Cross-validation ensures reliable evaluation, feature engineering improves the quality of input data, and comparing multiple models can help identify the most suitable architecture. These steps collectively address the inconsistencies observed across datasets.
- A. Correct.
Cross-validation helps ensure that the model generalizes well to unseen data by evaluating its performance across multiple data splits. This reduces the risk of overfitting.
- B. Correct.
Feature engineering is essential for improving the quality of the input data, which can significantly enhance the model's ability to learn meaningful patterns.
- C. Incorrect.
While adding noise can test robustness, it is not a priority when the goal is to improve and validate a model's performance across datasets. It is more relevant in adversarial testing or data augmentation.
- D. Correct.
Comparing models with different architectures is a fundamental step to identify the best-performing model for a given task.
- E. Incorrect.
Skipping validation and training on the entire dataset sacrifices the ability to assess the model's generalization ability, leading to potential overfitting and poor performance on unseen data.