Problem-1 [25 points]

Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or ratio). Some cases may have more than one interpretation, so briefly indicate your reasoning if you think there may be some ambiguity.

Example: Age in years. Answer: Discrete, quantitative, ratio

1. Number of patients in a hospital.
2. Brightness as measured by a light meter.
3. Brightness as measured by peoples judgments.
4. Angles as measured in degrees between 0 and 360.
5. Bronze, Silver, and Gold medals as awarded at the Olympics.

Problem-2 [75 points]

1. Discuss why a document-term matrix is an example of a data set that has asymmetric discrete or asymmetric continuous features. [15 points]
2. Explain the overfitting problem in the machine learning process and how would you minimize it in your analysis. [15 points]
3. Explain the underfitting problem in the machine learning process and how would you minimize it in your analysis. [15 points]
4. Discuss in detail the difference between supervised learning and unsupervised learning [15 points].
5. Using real-life examples, explain classification, regression, clustering, and association analysis. [15 points]