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考察數據科學家數據降維知識的40道題,快來測測吧(附答案)

https://yq.aliyun.com/cloud

Ankit GuptaIIIT Allahabad

https://www.linkedin.com/in/ankit-gupta

Githubhttps://github.com/anki1909

https://www.analyticsvidhya.com/blog/author/facebook_user_4/

PCA

T-SNE

LDA

PCAt-SNE
582 

928d65cd7fb1a12945c1a9227d07cb56a134eb99



 

153c921f0e61330ad5e88eab6b9cc912e81ac306


(所有分值的平均值)19.52

得分中位數(按順序排列的中間值)20

模型得分(最常出現的得分)19 

736456b7b03d92facd1721e93fd7f06aec4855b6


 

R

R-Python 


11100

A.

B.

A 

2)[

A.

B.

A

LDA

3

1E = A + 3 * B

2 

上述步驟可以表示降維方法嗎?

A.

B.

A

1 


A.

B.

C.

D.都不是

A

99

5)[

A.

B.

A

6 

A. t-SNE

B. PCA

C. LDA

D.都不是

D

7

A.

B. 

A

 

PCAPCA

1.PCA

2.

3.<=

4.

A. 1

B. 1

C. 2

D. 12

E. 12 

F.

F

9

A.k

B.k

C.

B

k 

10t-SNE

A.300

B.310

10,0008

10,000200

C

t-SNE

11 

A.

B.

C.

B

SNESNE 

12Word2vec1000

A. t-SNE

B. PCA

C. LDA

D.

A

t-SNE

 13

A.

B.

A 

t-SNE

14

A.t-SNE

B.t-SNE

C.t-SNE

D.t-SNE

D

D

15

A.

B.

C.

D.

D

16 

A.t-SNE

B.T-NSE

C.PCA

D.不是

A

17iXjYiYjXiXj
1.XiXjp
2.YiYjq
XiXj

A.p

B.p

C.p

D.Pj | i

C

18 

68fbad7de725da7216cf5a9ab97a346c4a71c232

A.LDA

B. LDA

C. LDA

D.LDA

A

19LDA

A.

B.

C.

D.

A

20PCA
1. LDA
2. LDAPCA
3. PCA

A. 1

B. 2

C. 1

D.

E. 12

E

21

A. PCA

B. PCA

C.

D.

B

22PCA 

1. 
2.
3.

A. 1

B. 2

C. 1

D. 12

C

23PCA
1.
2.
3.
4.

A. 1

B. 1

C. 2

D. 2

D


24 

5dc6c179ff76cbb78c5202290ad2cc6c39c68226


A.

B.

C.

D.

B

B

25
1.
2.
3. PCA

4. PCA

A. 1

B. 1

C. 2 

D. 2

D

PCA

26
X1X2PCA

4b7f270a37584e45ec2c03cbd5ca55c0159d74db

26

A.

B.

C.

D.都不是

B 

PCA——

27
1.
2.
3.
4.

A. 1

B. 2

C. 3

D. 1

C

28SVD

A.

B.

C.

D.

B

SVD 

29
23(-1,-1) 

17c6b1524f7aa26f2b9a1fcfe933f072479be2e6

29 

1.[√2/2
2.(1 /√3)
3.([-√2/ 2)
4.(- 1 /√3, - 1 /√3)

A. 1

B. 3

C. 1

D. 2

C

v = [√2/ 2T

30T11 

A.(- √2)0)(√2)

B.(√2)(0)(√2)

C.(√2)(0)(- 2)

D.(- 2)(0)(- 2)

A

z1 = [-1 T = - √2z2= 0

31(( √2)(0)(√2))

A. 0

B. 10 

C. 30

D. 40

A

0;

32

f6c24d18e2536511393ad98e6cea5fa76cfff3bd

A.LD1

B.LD2

C.

D.

A

33
PCAλ1≥λ2≥•••≥λN 

be36df1622935fffddae1ee736edc14f32e55401

f(M)(貢獻率)M 

c0cead299aefbbd7be8ad3bc513836d83d6a84bd

33PCAM分量D

A.

B.

C.A

D.

A

f1PCA

34

A. LDAPCA

B.

C.PCALDA

D.

A 

35
1.(0.5,0.5,0.5,0.5)(0.71,0.71,0,0)
2. (0.5,0.5,0.5,0.5)(0,0)
3. (0.5,0.5,0.5,0.5)(0.5,0.5,0.5,0.5)
4. (0.5,0.5,0.5,0.5)(-0.5,-0.5,0.5,0.5)

A. 1

B. 1

C. 2

D. 3

D

36
1.
2.

A. 1

B. 2

C. 1

D.

C

 (需翻牆)

77180d03550c958888e937df4f5c42e8b1dc8151

A. 

B.

C.

D.

B

PCA 

38假設10

A. 20

B.9

C. 21

D. 11

E. 10

B

LDA(需翻牆)

39
  

a7453a5310b51689325dddc1752a4b56fe4a2682

39

1.

2.

A. 1 

B. 2

C. 1

D.

C

40

21596fa168a0d37dfd638bc3ab71e2fb66ff3395
A. 7
B. 30

C. 40 

D.

B

30


一些幫助

@

40 Must know Questions to test a data scientist on Dimensionality Reduction techniquesAnkit Gupta pdf

 

最後更新:2017-04-24 13:00:41

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