Questions
- What are some challenges in porting data imbalance handling methods from classical machine learning models to deep learning models?
- How could an imbalanced version of the MNIST dataset be created?
- Use the MNIST dataset to train a CNN model with varying degrees of imbalance in the data. Record the model’s overall accuracy on a fixed test set. Plot how the overall accuracy changes as the imbalance in the training data increases. Observe whether the overall accuracy declines as the training data becomes more imbalanced.
- What is the purpose of using random oversampling with deep learning models?
- What are some of the data augmentation techniques that can be applied when dealing with limited or imbalanced data?
- How does undersampling work in handling data imbalance, and what are its limitations?
- Why is it important to ensure that the data augmentation techniques preserve the original labels of the dataset?
