Exercises Unit A - Conceptual
Bachelor’s Degree Programme in Philosophy, International and Economic Studies, Ca’ Foscari University of Venice.
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Chapter 2 Exercise 1.
For each of parts (a) through (d), indicate whether we would generally expect the performance of a flexible statistical learning method to be better or worse than an inflexible method. Justify your answer.
- The sample size n is extremely large, and the number of predictors p is small.
- The number of predictors p is extremely large, and the number of observations n is small.
- The relationship between the predictors and response is highly non-linear.
- The variance of the error terms, i.e. \sigma^2 = \mathbb{V}\mathrm{ar}(\epsilon), is extremely high.
Chapter 2 Exercise 4.
You will now think of some real-life applications for statistical learn- ing. (b) Describe three real-life applications in which regression might be useful. Describe the response, as well as the predictors. Is the goal of each application inference or prediction? Explain your answer.
Chapter 2 Exercise 5.
What are the advantages and disadvantages of a very flexible (versus a less flexible) approach for regression or classification? Under what circumstances might a more flexible approach be preferred to a less flexible approach? When might a less flexible approach be preferred?
Chapter 2 Exercise 6.
Describe the differences between a parametric and a non-parametric statistical learning approach. What are the advantages of a para- metric approach to regression or classification (as opposed to a non- parametric approach)? What are its disadvantages?