Question: 1 . Which library contains the K - means algorithm?A . Pandas.B . Sklearn.C . Seaborn. 2 . Z - score is a method for

1 .

Which library contains

the K

-

means algorithm?A

.

Pandas.B

.

Sklearn.C

.

Seaborn.

2 .

-

score is a method for

_____________.

.

Standardising.B

.

Diagnosing.C

.

Clustering.

3 .

The cluster centre, or centroid, is the

___________

of all the observations belonging to the cluster.A

.

Mean.B

.

Median.C

.

Mode.

4 .

Which of the following is a popular technique for visualising clusters when the data is higher

-

dimensional?A

.

The x

-

y plot.B

.

The elbow plot.

5 .__________

is a cluster tree diagram that groups entities that are nearer to each other.A

.

Elbow diagram.B

.

Dendrogram.C

.

Silhouette diagram.

6 .

A dendrogram cannot be drawn using the following library: A

.

Matplotlib.B

.

Seaborn. C

.

NumPy.

7 .

It may be good to have many clusters to make better business sense.A

.

TrueB. False

8 .

With product segmenting, the variance of the cluster will be

__________

when there is only one segment.A

.

LowestB. MediumC. Highest

9 .

Which method plots the percentage of variance explained as a function of the number of clusters? A

.

The Dendogram methodB. The Elbow curve method

10 .

The number of clusters is optimal when adding another cluster does not change the

__________.

.

MeanB. MedianC. Variance

11 .

The

__________

method gives an estimate of how well each data point fits into its cluster.A

.

Dendrogram. B

.

Elbow curve. C

.

Silhouette.

12 .

What is the range of the silhouette score?A

. 0

and

1 .

. - 1

and

0 .

. - 1

and

1 13 .

________

silhouette score indicates that a data point is very similar to other data points in the cluster.A

.

Higher

-

positive.B

.

Higher

-

negative.C

.

Near zero.

14 .____________

prevents variables with larger scales from dominating cluster definitions.A

.

Scaling down.B

.

Standardisation.

15 .

What type of analytics is classification?A

.

Descriptive. B

.

Diagnostic. C

.

Predictive. D

.

Prescriptive.

16 .

In general, what does a silhouette score close to

- 1

indicate?A

.

That the clustering is good. B

.

That the clustering is bad. C

.

That the clustering is acceptable.

17 .

__________

learning algorithms, the classes are known a priori.A

.

Supervised. B

.

Unsupervised.

18 .

A clustering objective is to ensure that the variation between clusters is

___________.

.

Minimised. B

.

Maximised.

19 .

Classification problems are

___________

learning algorithms.A

.

Supervised. B

.

Unsupervised.

20 .

In general, which silhouette score indicates that the clustering is good?A

. - 1 .

. 0 .

. + 1 .

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