An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
13th Edition
ISBN: 9781461471370
Author: Gareth James
Publisher: SPRINGER NATURE CUSTOMER SERVICE
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Expert Solution & Answer
Chapter 2, Problem 1E
a.
Explanation of Solution
Flexible or inflexible method
- When the sample size n is large and number of predictors p is small, then the method is inflexible.
- Flexible methods work better when a large number of predictors need to be predicted.
- An inflexible method will work better in the case of small number of predictors.
b.
Explanation of Solution
Flexible or inflexible method
- When the number of predictors is large, then the method is flexible.
- Flexible methods work better when a large number of predictors need to be predicted.
- An inflexible method will work better in the case of small number of predictors.
c.
Explanation of Solution
Flexible or inflexible method
- When the relationship between predictors and response is non-linear then the method is flexible.
- Flexible methods are good at fitting non-linear models.
d.
Explanation of Solution
Flexible or inflexible method
- When the variance of error terms is extremely high, then the method is inflexible.
- Choosing a flexible model in the case is over fitting.
- A flexible method will end up fitting the noise.
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Chapter 2 Solutions
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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