Question 1

WEEK 7 QUIZ

Question 1

Which of the following statements is true with reference to the sensitivity of k-nearest neighbours to noise? Select one:

a. k-NN is sensitive to noise irrespective the number of neighbours.
b. As the number of neighbours increases, k-NN becomes less sensitive to noise.
c. k-NN becomes less sensitive for smaller values of k.
d. k-NN is insensitive to noise

Memo

The correct answer is: b) As the number of neighours increases, k-NN becomes less sensitive to noise

Question 2

TRUE OR FALSE: The k-nearest neighbour algorithm can be applied only to problems with numerical input features

Memo

The correct answer is: False

Question 3

TRUE OR FALSE: The k-nearest neighbour algorithm can be applied to classification problems which has more than two classes.

Memo

The correct answer is: True

Question 4

TRUE OR FALSE: k-Nearest neighbour is insensitive to target feature based outliers when the algorithm is applied to regression problem

Memo

The correct answer is: False

Question 5

The k-Nearest neighbour algorithm becomes less sensitive to noise for larger values for k.

Memo

The correct answer is: True

Question 6

TRUE OR FALSE: For k-NN that uses Euclidean distance, it is not necessary to normalize descriptive feature values

Memo

The correct answer is: False

Question 7

TRUE OR FALSE: The k-NN algorithm actually does not perform any form of learning

Memo

The correct answer is: True

Question 8

TRUE OR FALSE: The inductive bias of k-NN applied to a classification problem is that instances of data close to each other in feature space belong to the same class

Memo

The correct answer is: True
Similar data points should have a similar target.

Question 9

TRUE OR FALSE: If for some problem there are many features with missing values, then it is suitable to ignore all of those features within the distance calculation.

Memo

The correct answer is: False
If there are not too many features that have missing values, then one can ignore the features with missing values within the distance calculation. k-NN is somewhat robust to missing values so long as not too many missing values across various features. In this question there are "many features with missing values".

==Applicable to the next set of questions:== The dataset below contains two instances and two features (f1 and f2). The target is provided.

ID	$\mathbf{f 1}$	$\mathbf{f 2}$	target
1	500	4	320
2	550	7	380

Assume a multivariate linear regression model is used to solve this problem, given the following weights: $w0(t=0)= -0.146$ $w1(t=0) = 0.185$ $w2(t=0) = -0.044$

Question 10

Calculate the model output for instance 1.

Memo

The correct answer is: 92.178
-0.146+0.185*\500-0.044*4

Question 11

Calculate the model output for instance 2.

Memo

The correct answer is: 101.296

-0.146+0.185*550-0.044*7

Question 12

Calculate the error that the model makes on instance 1.

Memo

The correct answer is: 227.822
$320-92.178$

Question 13

Calculate the error that the model makes on instance 2.

Memo

The correct answer is: 278.704
$380-101.296$

Question 14

Calculate the delta value for w0 on instance 1.

Memo

The correct answer is: 227.822
Since d0=1, then delta w0 for instance 1 is simply the same value of the error for instance one. (tiMw(d))*dj,i in this case j = 0 (feature 0) and thus d0 = 1.

Question 15

Calculate the delta value for w0 on instance 2

Memo

The correct answer is: 278.704
Since d0=1, then delta w0 for instance 1 is simply the same value of the error for instance two. (tiMw(d))*dj,i in this case j = 0 (feature 0) and thus d0 = 1.

Question 16

Calculate delta w1 for instance 1.

Memo

The correct answer is: 113911
Multiply the error value with the value of the feature
227.822*500

Question 17

Calculate delta w1 for instance 2.

Memo

The correct answer is: 153287.2
Multiply the value of the error with the value of the feature (550)
278.704*550

Question 18

Calculate the error signal for w0. (i.e. the value that will be used to perform the weight update). In the lecture notes this is denoted as delta (D,w0)

Memo

The correct answer is: 506.526
Here you sum of the individual error values 227.822+278.704

Question 19

Calculate the error signal for w1. (i.e. the value that will be used to perform the weight update). In the lecture notes this is denoted as delta (D,w1)

Memo

The correct answer is: 267198.2
Here you find the sum of the individual error values 113911+153287.2