1. What is the first step in framing a machine learning problem?

A. Choose a model architecture

B. Gather training data

C. Define the problem you want to solve

D. Evaluate model accuracy

2. Which of the following is an example of a classification problem?

A. Predicting tomorrow's temperature in degrees

B. Estimating the price of a house

C. Predicting whether an email is spam or not

D. Determining how long it will take to drive somewhere

3. In problem framing, a label is best described as:

A. The type of model used

B. The data collected from users

C. The output you're trying to predict

D. A tag used for debugging

4. What makes a good label in supervised learning?

A. It’s expensive to collect

B. It changes frequently

C. It is consistent and accurately represents the desired output

D. It is difficult to interpret