Case context: A team downloads cat and non-cat pictures from different websites. It labels them as positive and negative examples but refuses to run its learning algorithm on them because it believes every dataset must remain untouched until evaluation.

Question: What has the team misunderstood, and how should it use these pictures?

Sample answer: The team has confused a training dataset with a test dataset. It should assemble the labeled cat and non-cat pictures into a large training set and run the learning algorithm on that set so the model parameters can be updated. Data reserved as a test set should remain held out for evaluation.

Key points:

• The downloaded pictures can form a training set.
• Cat pictures are positive examples.
• Non-cat pictures are negative examples.
• The learning algorithm should run on the training set.
• A test dataset is held out for evaluation.

Rubric: The response should diagnose the training-versus-test confusion and recommend using the labeled pictures for learning while keeping test data reserved for evaluation.