NCA-AIIO Question 86
Select 4You are tasked with training a deep learning model on a healthcare dataset to predict patient outcomes. After analyzing the dataset, you notice class imbalance (e.g., significantly fewer positive cases compared to negative cases). Additionally, during model evaluation, you observe unusually high precision but low recall. Which actions should you take to identify and address factors that could affect the research results?
- A
Apply data augmentation techniques to generate synthetic samples for the minority class.
- B
Use a performance metric like F1-score to balance precision and recall.
- C
Increase the learning rate to make the model converge faster.
- D
Analyze the confusion matrix to assess how the model performs on different classes.
- E
Ensure the dataset is shuffled properly before splitting into training and testing sets.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
Class imbalance and evaluation metrics are critical factors in deep learning research, particularly when working with real-world datasets. By addressing class imbalance (e.g., through data augmentation), selecting the right evaluation metric (like F1-score), and analyzing performance via tools like the confusion matrix, you can uncover trends or issues that affect research outcomes. Proper dataset shuffling ensures unbiased training and testing splits, which is essential for reliable results.
- A. Correct.
Applying data augmentation for the minority class can help mitigate class imbalance, which is a significant factor affecting the results.
- B. Correct.
Using the F1-score can provide a balanced measure of both precision and recall, helping to address the issue of low recall.
- C. Incorrect.
Increasing the learning rate is not directly related to resolving class imbalance or improving research results; it may instead destabilize the training process.
- D. Correct.
Analyzing the confusion matrix can help identify specific issues like misclassification rates, which may reveal trends or factors affecting results.
- E. Correct.
Properly shuffling the dataset ensures data points are distributed randomly, preventing unintended biases during training and testing.