Free Printable InCube #Quiz 5

1.

'The number of chocolates in a bag' is an example of -

a)

Continuous Data

b)

Classical Data

c)

Discrete Data

d)

All of these

2.

'The speed of cars' is example of -

a)

Continuous Data

b)

Discrete Data

c)

Both of these

d)

None of these

3.

The k in KNN algorithm represents ___________

a)

accuracy rate

b)

number of data points

c)

the number of nearest neighbors to consider while predecting

d)

none of these

4.

KNN stands for ___________________.

a)

K - Near Neighbour

b)

K - Nearest Neighbour

c)

Both of these

d)

None of these

5.

Which of the following machine learning algorithm can be used for imputing missing values of both categorical and continuous variables?

a)

Linear Regression

b)

K-NN

c)

Logistic Regression

d)

All of these

6.

Which of the following option is true about K-NN algorithm ?

a)

It can be used for classification

b)

It can be used for regression

c)

It can be used in both classification and regression

d)

None of these

7.

In kNN, we use _____________ to calculate the distance between nearest neighbours

a)

Slope

b)

Origin distance

c)

Validation data

d)

Euclidean distance

8.

Formula to calculate Euclidean Distance is ______

a)

$\sqrt[]{\left(x_2+x_1\right)^2-\left(\ y_2+y_1\right)^2}$

b)

$\sqrt[]{\left(\ y_2-y_1\right)^2-\left(x_2-x_1\right)^2}$

c)

$\sqrt[]{\left(\ y_2+y_1\right)^2+\left(x_2+x_1\right)^2}$

d)

$\sqrt[]{\left(x_2-x_1\right)^2+\left(\ y_2-y_1\right)^2}$

9.

Which of the following will be Euclidean Distance between the two data point

A(1,3) and B(2,3)?

a)

1

b)

2

c)

4

d)

8

10.

Which of the following distance metric can not be used in k-NN?

a)

Manhattan

b)

Minkowski

c)

Euclidean

d)

All can be used

11.

A table with 4 different combinations of predicted and actual values which is extremely useful for measuring Recall, Precision, Specificity, Accuracy, and most importantly AUC-ROC curves is called ________

a)

KNN Table

b)

Slope - Intercept Table

c)

Confusion Matrix

d)

K-Means Table

12.

We need confusion matrix because ______

a)

it confuses the machine learning algorithm

b)

it gives accuracy as 100%

c)

it compares predicted values with actual values

d)

All of these

13.

In the image below, which would be the best value for k assuming that the algorithm you are using is k-Nearest Neighbor.

a)

3

b)

10

c)

20

d)

50

14.

Below are the types of errror in confusion matrix -

a)

Type 1 Error

b)

Type 2 Error

c)

Both of the above

d)

None of the above

15.

Independent variables are also known as _________

a)

response variables

b)

predictor variables

c)

both of these

d)

None of these

16.

If Mohan predicts cat (True Label) by using KNN model but actually it is dog, then it is

a)

True Positive

b)

True Negative

c)

False Positive

d)

False Negative

17.

A patient is diagonised with cancer. Which is more sensitive in this case ?

a)

True Positive

b)

True Negative

c)

False Positive

d)

False Negative

InCube #Quiz 5

Total questions: 17

Worksheet time: 17mins

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