Which techniques can be used by the ML Specialist to improve this specific test error?
Increase the training data by adding variation in rotation for training images.
Increase the number of epochs for model training
Increase the number of layers for the neural network.
Increase the dropout rate for the second-to-last layer.
Explanations:
By adding variations in rotation to the training data, the model will learn to recognize cats in different orientations, including upside-down positions. This will help reduce misclassification errors related to the specific issue of upside-down cats.
Increasing the number of epochs might allow the model to fit the data more, but it won’t directly address the issue of recognizing upside-down cats unless the data itself is augmented to include such variations.
Increasing the number of layers may increase the model’s capacity to learn more complex features, but it doesn’t specifically address the problem of orientation of the cats. It could also lead to overfitting with the limited data available.
Increasing the dropout rate could help reduce overfitting, but it does not specifically solve the issue of upside-down cats. The primary issue is data variation, not model regularization.
I design that the answer is:
Increase the training data by adding variation in rotation for training images.