A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
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.
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