Question 4You are working on an automated check-out kiosk for a supermarket, and are building a classifier for apples, bananas and oranges. Suppose your classifier obtains a training set error of 0.5%, and a dev set error of 7%. Which of the following are promising things to try to improve your classifier? (Check all that apply.)

The question seems to be about improving a classifier model that has a low training set error but a high development set error. This situation typically indicates a high variance problem, or overfitting, where the model performs well on the training data but poorly on unseen data. Here are some promising things to try to improve your classifier:

1. Collect more training data: More data can help the model generalize better and reduce overfitting.

2. Regularization: This technique can help to prevent overfitting by adding a penalty term to the loss function, which discourages complex models.

3. Increase the size of the dev set: If the dev set is too small, it might not be representative of the real-world data the model will encounter, leading to a high dev set error.

4. Try a different model architecture: If the current model is too complex, a simpler model might generalize better. Conversely, if the model is too simple, a more complex model might perform better.

5. Data augmentation: This technique can artificially increase the size of the training set by creating modified versions of the existing data, such as by rotating or zooming into images.

6. Early stopping: This technique involves stopping the training process before the model starts to overfit.

Remember, it's important to try these solutions one at a time to understand which one actually improves the performance.