What can you do to reduce underfitting in a deep-learning model?
A. Increase the number of iterations
B. Use dimensionality reduction techniques
C. Use cross-validation technique to reduce underfitting
D. Use data augmentation techniques to increase the amount of data used.
Answer:D
What can you do to reduce underfitting in a deep-learning model?
A. Increase the number of iterations B. Use dimensionality reduction techniques C. Use cross-validation technique to reduce underfitting D. Use data augmentation techniques to increase the amount of data used.
Explanation: Options A and B can be used to reduce overfitting in a model. Option C is just used to check if there is underfitting or overfitting in a model but cannot be used to treat the issue. Data augmentation techniques can help reduce underfitting as it produces more data, and the noise in the data can help in generalizing the model.