Case context: A team is training an image classifier on a pool of 240 labeled photos. They want to inspect model behavior when the training set size is only 12 examples, but a single run gives erratic training and validation errors. To get a steadier estimate, they decide to repeat the experiment on many randomly drawn subsets from the full pool.

Question: What should they do to form the 12-example subsets, and how should they compute the value that gets plotted for that training-set size?

Sample answer: To reduce the noise at the 12-example setting, I would draw several separate subsets of 12 examples from the 240 available training examples, allowing repeated selection of the same original example within a subset. I would train a distinct model on each subset, then calculate the training error and validation error for each model. After that, I would average the training errors and average the validation errors across all of the models, and use those averages as the plotted values for the learning curve.

Key points:

• Form 3-10 different subsets of 12 examples from the 240-example pool, using resampling with replacement.
• Train one model for each subset.
• Measure training error and validation error for every trained model.
• Average the errors across models and plot the mean values.

Rubric: The response must explain: 1) forming 3-10 resampled subsets of 12 examples from the 240-example pool with replacement, 2) training a different model on each subset, 3) computing training and validation error for each model, and 4) averaging those errors before plotting the result.