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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Chapter 3, Problem 4E
a.
Explanation of Solution
Relationship between X and Y being linear
- Without knowing more details about the training data, it is difficult to know which training RSS is lower between linear or cubic...
b.
Explanation of Solution
Relationship between X and Y being linear
- In this case the test RSS depends upon the test data, so we have not enough information to conclude...
c.
Explanation of Solution
Relationship between X and Y being non-linear
- Polynomial regression has lower train RSS than the linear fit because of higher flexibility...
d.
Explanation of Solution
Relationship between X and Y being non-linear
- There is not enough information to tell which test RSS would be lower for either regression given the problem statement is defined as not knowing “how far it is from linear”...
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Students have asked these similar questions
We create a simple regression model and call the
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Im=LinearRegression()
Im.fit(X,Y)
in a multilinear model we proceed in the same
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mlm=LinearRegression()
mlm.fit(Z,Y)
How does the linear regression model knows if we
are doing a simple or multiple linear regression?
Answer:
"When conducting a binary regression with
a skewed predictor, it is often easiest to
assess the need for x and log(x) by including
them both in the model so that their relative
contributions can be assessed directly."
Show that indeed the log odds are a
function of x and log(x) for the gamma
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Now suppose that you have two versions of your model with different parameters(e.g., different regularization) or even different model families (e.g., logistic regression versus random forest). Which one is better?
Chapter 3 Solutions
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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- Question 1: In class we went over the derivation for linear regression on the white board. Starting with = the step where you have E (Y – XW)¹ (Y – XW) write the derivation to get to the step W = (XTX)-¹XTY. Explain the math used in each step.arrow_forwardSay that you have the following initial settings for binary logistic regression: x = [1, 1, 3] w = [0, -2, 0.75] b = 0.5 2. Given that x's label is 1, what is the value of w_1, w_2, and w_3 at time t + 1 if the learning rate is 1? For this problem, you may ignore the issue of updating the bias term. 3. What is the value of P(y = 1 | x) given your updated weights from the previous question? 4. Given that x's label is 1, what is the value of the bias term at time t + 1 if the learning rate is 1? 5. What is the value of P(y = 1 | x) given both your updated weights and your updated bias term? 6. Given that x's label is 0, what is the value of P(y = 0| x) at time t + 1 if the learning rate is 0.1? Round your answer to the nearest 1000th as a number [0, 1].arrow_forwardIn linear regression, 8 tells us how meaningful the corresponding x is in predicting the output. This means, ...... O if a particular 8-value is very small compared to others, the corresponding x plays a little role in predicting the output. O if a particular 8-value is very large compared to others, the corresponding x plays a little role in predicting the output.arrow_forward
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