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 4, Problem 5E
a.
Explanation of Solution
Baye’s decision boundary
- If the Bayes decision boundary is linear, then QDA is expected to perform better on the training set.
- Because of its higher flexibility, it may yield a closer fit...
b.
Explanation of Solution
Baye’s decision boundary
- If the Bayes decision boundary is non-linear, QDA...
c.
Explanation of Solution
Baye’s decision boundary
- QDA is more flexible than LDA and so has higher variance...
d.
Explanation of Solution
Baye’s decision boundary
- With fewer sample points, the variance from using a more flexible method s...
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2. Can you design a binary classification experiment with 100 total population (TP+TN+FP+ FN), with precision (TP/(TP+FP)) of 1/2, with sensitivity (TP/(TP+FN)) of 2/3, and specificity (TN/(FP+TN)) of 3/5? (Please consider the population to consist of 100 individuals.)
given the observed data (obsX,obsY), learning rate (alpha), error change threshold, and delta from the huber loss model,write a function returns theta0 and theta1 that minimizes the error.
Use pseudo huber loss function
Imagine a regression model on a single feature, defined by the function f (x) = wx + b where
X, W, and b are scalars. We will use the MSE loss loss(w, b) = E;(f(x;) – t;)² .
n
Work out the gradient with respect to b. Which is the correct answer? Read the four equations
carefully, so you notice all the differences.
1. E:(f(x;) – t;)x;
2.E(f(x;) – t;)
n
3.- E;(wx; + b – t;)x;
n
4. E: (wa; +b - t;)
n
4
Chapter 4 Solutions
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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