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Q1 Textbook Theory Questions http://faculty.marshall.usc.edu/gareth-james/ISL/
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.
(a) The sample size n is extremely large, and the number of predictors p is small.
(b) The number of predictors p is extremely large, and the number of observations n is small.
(c) The relationship between the predictors and response is highly non-linear.
(d) The variance of the error terms, i.e. ?2 = Var(), is extremely high
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?
6. Describe the differences between a parametric and a non-parametric statistical learning approach. What are the advantages of a parametric approach to regression or classification (as opposed to a nonparametric approach)? What are its disadvantages?
Q2 Textbook Applied Questions Attempt with Python
8. Exploratory Data Analysis: College data set: College.csv. It contains a number of variables for 777 different universities and colleges in the US. Do all the exercises in Python:
8a. Read the csv file with pandas
8b.Fix the first row as row headers
8c.