MLA-C01 Question 97
Select 3You are training a machine learning model to classify medical images into categories based on disease type. Upon analyzing your dataset, you discover a significant class imbalance, with certain disease categories having far fewer images than others. What are the most appropriate strategies to address this issue?
- A
Generate synthetic images for the underrepresented classes using techniques like GANs (Generative Adversarial Networks).
- B
Resample the dataset by oversampling the underrepresented classes or undersampling the overrepresented classes.
- C
Use class weights during model training to penalize the model for misclassifying underrepresented classes.
- D
Remove some images from the overrepresented classes to ensure an even class distribution.
- E
Increase the number of epochs during training to allow the model to focus more on underrepresented classes.
Show answer and explanation
Correct answers: A, B, C
Explanation
Addressing class imbalance in datasets is crucial for training effective machine learning models. Techniques like synthetic data generation, resampling, and using class weights during training are well-established strategies to mitigate the impact of class imbalance. Removing data from overrepresented classes is generally avoided as it can lead to a loss of important information. Similarly, increasing the number of epochs does not specifically address the core issue of imbalance and can introduce overfitting.
- A. Correct.
Generating synthetic images using techniques like GANs can help augment the dataset for underrepresented classes, reducing class imbalance without losing valuable data.
- B. Correct.
Resampling, either by oversampling the underrepresented classes or undersampling the overrepresented classes, is a common strategy to address class imbalance in datasets.
- C. Correct.
Using class weights during training is an effective way to handle class imbalance, as it helps the model focus more on underrepresented classes by penalizing misclassifications.
- D. Incorrect.
While removing data from overrepresented classes can balance the dataset, it risks losing valuable information and reducing the overall dataset size, which is generally not recommended.
- E. Incorrect.
Increasing the number of epochs does not directly address class imbalance. It can lead to overfitting, especially if the dataset contains significant imbalance.