NPTEL | DATA MINING | WEEK-4
NPTEL
DATA MINING
WEEK-4
Which of the following statement is true about Bayes classifier?
It always provides zero error when class distributions are known
It always provides the lowest possible error when class distributions are known
It may not always provide the lowest possible error when class distributions are known
It always provides the lowest possible error when class distributions are estimated
Let A be an example, and C be a class. The probability P(C|A) is known as:
Apriori probability
Aposteriori probability
Class conditional probability
None of the above
Let A be an example, and C be a class. The probability P(C) is known as:
Apriori probability
Aposteriori probability
Class conditional probability
None of the above
Consider a binary classification problem with two classes C1 and C2. Class labels of ten other training set instances sorted in increasing order of their distance to an instance x are as follows: {C1, C2, C1, C2, C2, C2, C1, C2, C1, C2}. How will a K=3 nearest neighbor classifier classify x?
There will be a tie
C1
C2
Not enough information to classify
According to the following graph, what should be the appropriate value of K if KNN algorithm is used?
5
10
15
20
Which of the following will be Euclidean Distance between the two data point A(1,3) and B(2,3)?
1
2
4
8
Which of the following will be Manhattan Distance between the two data point A(1,3) and B(2,3)?
1
2
4
8
You are given the following set of training examples where x and y are the two inputs and Class is the target. What would be the target class of a data point x=1, y=1 using Euclidean distance in 3-NN?
Class +
Class –
None of the above
Can’t be determined
What would be the class if 7-NN is used?
Class +
Class –
None of the above
Can’t be determined.
In the following figure you are given the distances between the two points A(x1,y1) and B(x2,y2).
Which of the statement is true?
Left: Manhattan distance and Right: Euclidean Distance
Left: Euclidean distance and Right: Manhattan distance
Both are Euclidean distance
None of the above

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