k-Nearest Neighbors (k-NN) MCQs

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1. : What is the primary application of the k-Nearest Neighbors (k-NN) algorithm in data mining?
(A) Clustering
(B) Regression
(C) Classification and Regression
(D) Association rule mining

2. : In the k-NN algorithm, what does the ‘k’ represent?
(A) The number of features
(B) The number of neighbors to consider
(C) The size of the dataset
(D) The depth of the decision tree

3. : Which distance metric is most commonly used in the k-NN algorithm?
(A) Manhattan distance
(B) Euclidean distance
(C) Hamming distance
(D) Cosine similarity

4. : How does the k-NN algorithm classify a new data point?
(A) By calculating the mean of the k nearest neighbors
(B) By taking the majority vote of the k nearest neighbors
(C) By using a decision tree
(D) By clustering the data points

5. : What is a potential drawback of using the k-NN algorithm?
(A) It is not suitable for large datasets due to high computational cost
(B) It cannot handle numerical data
(C) It requires a lot of pre-processing
(D) It cannot be used for classification tasks

6. : How can the performance of the k-NN algorithm be affected by the choice of ‘k’?
(A) Too small a ‘k’ can lead to overfitting
(B) Too large a ‘k’ can lead to underfitting
(C) Both a and b
(D) The choice of ‘k’ does not affect the performance

7. : What is the curse of dimensionality in the context of the k-NN algorithm?
(A) The performance improves as the number of dimensions increases
(B) The distance between data points becomes less meaningful as the number of dimensions increases
(C) It refers to the difficulty of handling large datasets
(D) It refers to the overfitting problem

8. : Which of the following methods can be used to select the optimal value of ‘k’ in the k-NN algorithm?
(A) Cross-validation
(B) Random selection
(C) Using a fixed value
(D) Mean squared error

9. : How does the k-NN algorithm handle continuous attributes?
(A) By converting them to categorical data
(B) By using distance metrics like Euclidean distance
(C) By ignoring them
(D) By normalizing them to binary values

10. : What type of learning does the k-NN algorithm use?
(A) Supervised learning
(B) Unsupervised learning
(C) Reinforcement learning
(D) Semi-supervised learning

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k-Nearest Neighbors (k-NN) MCQs
By: Prof. Dr. Fazal Rehman | Last updated: May 14, 2025

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