Question

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 is as follows: {C1, C2, C1, C2, C2, C2, C1, C2, C1, C2}. How will a K=7 nearest neighbor classifier classify x?

A.  There will be a tie

 

B.  C1

 

C.  C2

 

D.  Not enough information to classify

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Answer 1

Answer:

The answer may be (d)

because it's not have mentioned when the x is placed

so may be it's D

Explanation:

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Answer 2

Option C : Class C2 will be predicted for the given point according to the KNN Model with k as 7.

K- Nearest Neighbors Classifier (KNN) :

• KNN or the K- Nearest Neighbors Classifier is a Supervised Machine Learning algorithm.
• It is used to classify the category of any new test dataset based on the category allocation of the training dataset.
• It predicts and classifies the category of the new point based on its proximity to points of other categories.

Here, the value of k is 7 i.e. we need to use the 7 closest data points to predict the category class of the given point. These 7 points are the 7 closest neighbors as measured using euclidean distance from the given point.

The classes of points sorted according to their distance from the given point are given : ${C1, C2, C1, C2, C2, C2, C1, C2, C1, C2}$

Thus, the classes of points of the 7-closest neighbors are :

$(C1, C2, C1, C2, C2, C2, C1)$

Number of points in class C1 in k-nearest = 3

Number of points in class C2 in k-nearest = 4

                               $4 > 3$

  No. of Points in C2 > No. of Points in C1

Thus, taking the majority the new point will be assigned to the class category C2.

Hence, the given KNN Model will predict the class C2 for the given point.

To learn more about KNN Classifier, visit

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