# GR5241编程代做、代写R留学生程序、R实验编程调试代写Python编程|帮做C/C++编程

Statistical Machine Learning GR5241
Spring 2021
Homework 2
Due: Monday, February 22nd by 11:59pm
Problem 1 (Training Error vs. Test Error, ESL 2.9)
In this problem, we want to use the least squares estimator to illustrate the point that the trainning error is
generally an underestimate of the prediction error (or test error).
Consider a linear regression model with p parameters,
We fit the model by least squares to a set of trainning data (x1, y1), . . . ,(xN , yN ) drawn independently from a
population. Let βˆ be the least squares estimate obtained from the training data. Suppose we have some test
data (˜x1, y˜1), · · · ,(˜xM, y˜M) (N ≥ M > p) drawn at random from the same population as the training data.
where Nk(x) is the neighborhood of x defined by the k closest points x, in the training sample. Assuming x is
not random, derive an expression of prediction error using squared error loss, i.e., compute and simplify
E[(Y − ˆfk(x0))2
|X = x0].
Note that x0 is a single query point (or test point).
Problem 3 (K-Means Clustering Proof)
Consider the traditional k-means clustering algorithm and let d(xi
, xj ) be squared Euclidean distance. Prove the
following identity:
• The syntax C(i) = k, or equivalently {i : C(i) = k}, represents the set of all indices (or observations)
having cluster assignment k. The symbol |Nk| represents the number of elements in set {i : C(i) = k}.
For example, suppose our data matrix consists of n = 10 observations and each observation (or row) is
assigned to K = 3 clusters
Case: {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
Cluster Assignment: {3, 1, 3, 1, 2, 2, 2, 1, 3, 2}
Then
{i : C(i) = 1} = {2, 4, 8} |N1| = 3
{i : C(i) = 2} = {5, 6, 7, 10} |N2| = 4
{i : C(i) = 3} = {1, 3, 9} |N3| = 3
Problem 4 (PCA, LDA and Logistic Regression)
The zipcode data are high dimensional, and hence linear discriminant analysis suffers from high variance. Using
the training and test data for the 3s, 5s, and 8s, compare the following procedures:
1. LDA on the original 256 dimensional space.
2. LDA on the leading 49 principle components of the features.
3. Multiple linear logistic regression (multinomial regression) using the same filtered data as in the previous
question.
Note:
• For all the above exercises, use R or Python functions to perform the PCA, LDA and multinomial regression,
i.e., there is no need to manually code these procedures.
• For all the above exercises, compare the procedures with respect to training and test misclassification
error. You need to report both training and test misclassification error in your submission.
• When evaluating the test error based on the filtered trained model, don’t forget to first project the test
features onto space generated by the leading 49 principle components. In R use the predict() function.
• The data of interest is already split into a training and testing set.
2
Problem 5 (PCA: Finance)
1. For each of the 30 stocks in the Dow Jones Industrial Average, download the closing prices for every trading
day from January 1, 2020 to January 1, 2021. You can use http://finance.yahoo.com to find the data. To
download the prices, for example for symbol AAPL, we use the R package quantmod. The R code is as the
following:
library(quantmod)
data <- getSymbols("AAPL", auto.assign = F, from ="2020-01-01", to = "2021-01-01")
2. Perform a PCA on the un-scalled closing prices and create the biplot. Do you see any structure in the biplot,
perhaps in terms of the types of stocks? How about the screeplot – how many important components seem
to be in the data?
3. Repeat part 2 using the scaled variables.
4. Use the closing prices to calculate the return for each stock, and repeat Part 3 on the return data. In looking
at the screeplot, what does this tell you about the 30 stocks in the DJIA? If each stock were fluctuating up
and down randomly and independent of all the other stocks, what would you expect the screeplot to look
like?
Problem 6 (PCA on Digits)
In this problem students will run PCA on the zipcode training data from Problem 4. This problem will help solidify
how to interpret PCA in a high dimensional setting.
1. Open and run the file “Problem 6 Setup.Rmd” to establish your data matrix X and unobserved test cases.
Note that the data matrix X has dimension 1753 × 256 after removing three cases.
2. Run PCA on the data matrix X. Produce a graphic showing the cumulative explained variance as a function
of the number of PC’s (or features). Identify how many PC’s yield 90% explained variance.
3. Display the first 16 principle components as images. Try to combine all 16 images in a single plot.
4. Approximate the test cases ConstructCase 1, ConstructCase 2 and ConstructCase 3 by projecting
these cases into the subspace (eigenspace) generated by d = 3, 58 and 256 principal components. Your
final result should be represented as 9 images, 3 per test case.

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