代做ETC3250 Exam 1代写留学生Matlab语言
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1. Question
Which of the following categorical response variables matches the binary matrix coding below:
$$\begin{align} \begin{array}{ccc} \left[ A & B & C\\ 1 & 0 & 0\\ 1 & 0 & 0\\ 0 & 1 & 0\\ 0 & 0 & 1\\ 0 & 0 & 1\\ 1 & 0 & 0\\ \right] \end{array} \end{align}$$
a. (A, A, B, C, C, A)'
b. (A, A, C, B, C, A)'
c. (B, A, C, A, A, C)'
d. (C, B, C, A, A, C)'
e. None of these because the coding is not binary
2. Question
Which of the following categorical response variables matches the binary matrix coding below:
$$\begin{align} \begin{array}{ccc} \left[ A & B & C\\ 1 & 0 & 0\\ 1 & 0 & 0\\ 0 & 1 & 0\\ 0 & 1 & 1\\ 0 & 0 & 1\\ 1 & 0 & 0\\ \right] \end{array} \end{align}$$
a. (C, B, C, A, A, C)'
b. (B, A, C, A, A, C)'
c. (A, B, C, B, C, A)'
d. (A, A, B, C, C, A)'
e. None of these because the coding is not binary
3. Question
Which of these plots would be considered the model plotted in the data space?
。A: The line of points is an SVM boundary
。 B: Convex hulls marking the results of a t-means clustering
。 C: Votes matrix from a random forest fit
a. A and B and C
b. A and B
c. B and C
d. A and C
e. A
f. B
g. C
4. Question
Which of these plots would be considered the data plotted in the model space?
。A: The line of points is an SVM boundary
。 B: Convex hulls marking the results of a t-means clustering
。 C: Votes matrix from a random forest fit
a. B and C
b. A and C
c. B
d. A
e. C
f. A and B
g. A and B and C
5. Question
The term _________ means the model overlaid on the data, with the primary purpose being to examine how well the model fits the main structures present in the data.
a. biplot
b. principal component analysis
c. model-in-the-data-space
d. tours of linear projections
e. data-in-the-model-space
6. Question
Which of the following projection matrices match the axes for this projection:
a. | X1| X2|var | |———-:|———-:|:—| | 0.4749795| 0.0490201|tr1 | | 0.1775791| 0.7827620|tr2 | | 0.1144942| -0.5927668|hed | | 0.6765058| -0.1581131|ad1 | | -0.1314610| 0.0801130|ad2 | | -0.5047863| -0.0457199|ad3 |
b. X1| X2|var | |———-:|———-:|:—| | 0.3241229| 0.0332502|tr1 | | 0.0332502| 0.8459679|tr2 | | -0.0792838| -0.5049216|hed | | 0.6886585| -0.0003777|ad1 | | 0.1761553| 0.0774175|ad2 | | -0.6182812| 0.1493095|ad3 |
c. | X1| X2|var | |———-:|———-:|:—| | 0.4961547| 0.1390207|tr1 | | 0.4459056| 0.5050245|tr2 | | 0.3300675| -0.6057191|hed | | 0.2433533| -0.3493922|ad1 | | -0.6214905| 0.0336788|ad2 | | -0.0241416| -0.4853027|ad3 |
d. none of them match
e. | X1| X2|var | |———-:|———-:|:—| | 0.9967310| 0.0001654|tr1 | | 0.0001654| 0.9993272|tr2 | | -0.0062352| -0.0346255|hed | | 0.0588159| -0.0007571|ad1 | | 0.0149625| 0.0051349|ad2 | | -0.0529634| 0.0109204|ad3 |
7. Question
Which of the following projection matrices match the axes for this projection:
a.
b.
c. none of them match
d.
e.
8. Question
When doing 5 - fold cross-validation, with these splits of the data:
fold 1: 1, 3, 4
fold 2: 2, 10, 15
fold 3: 7, 8, 9
fold 4: 5, 11, 14
fold 5: 6, 12, 13
Which subset of observations would be used to compute the predictive accuracy of the model when working with fold 4?
a. 1, 3, 4
b. 2, 10, 15
c. 7, 8, 9
d. 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 15
e. 5, 11, 14
9. Question
When doing 5 - fold cross-validation, with these splits of the data:
fold 1: 1, 3, 4
fold 2: 2, 10, 15
fold 3: 7, 8, 9
fold 4: 5, 11, 14
fold 5: 6, 12, 13
Which subset of observations would be used to train the model when working with fold 4?
a. 1, 3, 4
b. 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 15
c. 7, 8, 9
d. 5, 11, 14
e. 2, 10, 15
10. Question
From the following summary of a PCA, what proportion of the total variance would four principal components explain? (Note: The data was standardised prior to computing the PCA. If no values match exactly, pick the closest.)
> auswt20_pca$sdev
[1] 2.723 2.053 1.175 0.974 0.902 0.836 0.700 0.533
[9] 0.466 0.421 0.351 0.321 0.273 0.220 0.081 0.063
[17] 0.015
a. 82%
b. 0.457
c. 5.7%
d. 0.057
e. 46%
f. 0.82
g. 0.407
h. 41%
11. Question
From the following summary of a PCA, what proportion of the total variance would five principal components explain? (Note: The data was standardised prior to computing the PCA. If no values match exactly, pick the closest.)
> auswt20_pca$sdev
[1] 2.723 2.053 1.175 0.974 0.902 0.836 0.700 0.533
[9] 0.466 0.421 0.351 0.321 0.273 0.220 0.081 0.063
[17] 0.015
a. 0.82
b. 0.053
c. 0.52
d. 82%
e. 5.3%
f. 88%
g. 0.88
h. 52%
12. Question
For data having n = 92 and p = 5, how many parameters would need to be estimated to compute the variance-covariance matrix?
a. 15
b. 14
c. 24
d. 25
e. 92
f. 91
g. 4
13. Question
For data having n = 92 and p = 6, how many parameters would need to be estimated to compute the variance-covariance matrix?
a. 92
b. 91
c. 20
d. 36
e. 21
f. 35
g. 4