K-Nearest Neighbours (KNN) and **tree-based algorithms **are two of the most intuitive and easy-to-understand **machine learning algorithms**. Both are simple to explain and demonstrate, making them perfect for those who are new to the field. For beginners, it is crucial to test their knowledge of these algorithms as they are simplistic yet immensely powerful. These are commonly asked in interviews as well. Searching for KNN interview questions and practicing them can help one gain a deeper understanding of the algorithm and its practical applications. In this article, we are explaining the top 30 KNN interview questions or KNN MCQS that help you to succeed in the interview.

A) TRUE

B) FALSE

**Solution: A**

The training phase of the algorithm consists only of storing the feature vectors and class labels of the training samples.In the testing phase, a test point is classified by assigning the label which are most frequent among the *k* training samples nearest to that query point – hence higher computation.

A) 3

B) 10

C) 20

D 50

**Solution: B**

Validation error is the least when the value of k is 10. So it is best to use this value of k

A) Manhattan

B) Minkowski

C) Tanimoto

D) Jaccard

E) Mahalanobis

F) All can be used

**Solution: F**

All of these distance metric can be used as a distance metric for k-NN.

A) It can be used for classification

B) It can be used for regression

C) It can be used in both classification and regression

**Solution: C**

We can also use k-NN for regression problems. In this case the prediction can be based on the mean or the median of the k-most similar instances.

- k-NN performs much better if all of the data have the same scale
- k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large
- k-NN makes no assumptions about the functional form of the problem being solved

A) 1 and 2

B) 1 and 3

C) Only 1

D) All of the above

**Solution: D**

The above mentioned statements are assumptions of KNN algorithm

A) K-NN

B) Linear Regression

C) Logistic Regression

**Solution: A**

k-NN algorithm can be used for imputing missing value of both categorical and continuous variables.

A) It can be used for continuous variables

B) It can be used for categorical variables

C) It can be used for categorical as well as continuous

D) None of these

**Solution: A**

Manhattan Distance is designed for calculating the distance between real valued features.

- Hamming Distance
- Euclidean Distance
- Manhattan Distance

A) 1

B) 2

C) 3

D) 1 and 2

E) 2 and 3

F) 1,2 and 3

**Solution: A**

Both Euclidean and Manhattan distances are used in case of continuous variables, whereas hamming distance is used in case of categorical variable.

A) 1

B) 2

C) 4

D) 8**Solution: A**

sqrt( (1-2)^2 + (3-3)^2) = sqrt(1^2 + 0^2) = 1

A) 1

B) 2

C) 4

D) 8**Solution: A**

sqrt( mod((1-2)) + mod((3-3))) = sqrt(1 + 0) = 1

**Context: 11-12**

Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

A) + ClassB) – ClassC) Can’t say

D) None of these

**Solution: A**

All three nearest point are of +class so this point will be classified as +class.

A) + ClassB) – ClassC) Can’t say**Solution: B**

Now this point will be classified as â€“ class because there are 4 â€“ class and 3 +class point are in nearest circle.

**Context 13-14:**

Suppose you have given the following 2-class data where “+” represent a postive class and “” is represent negative class.

A) 3

B) 5

C) Both have same

D) None of these**Solution: B**

5-NN will have least leave one out cross validation error.

A) 2/14

B) 4/14

C) 6/14

D) 8/14

E) None of the above**Solution: E**

In 5-NN we will have 10/14 leave one out cross validation accuracy.

A) When you increase the k the bias will be increases

B) When you decrease the k the bias will be increases

C) Can’t say

D) None of these**Solution: A**

large K means simple model, simple model always condider as high bias

A) When you increase the k the variance will increases

B) When you decrease the k the variance will increases

C) Can’t say

D) None of these**Solution: B**

Simple model will be consider as less variance model

A) Left is Manhattan Distance and right is euclidean Distance

B) Left is Euclidean Distance and right is Manhattan Distance

C) Neither left or right are a Manhattan Distance

D) Neither left or right are a Euclidian Distance**Solution: B**

Left is the graphical depiction of how euclidean distance works, whereas right one is of Manhattan distance.

A) I will increase the value of k

B) I will decrease the value of k

C) Noise can not be dependent on value of k

D) None of these**Solution: A**

To be more sure of which classifications you make, you can try increasing the value of k.

- Dimensionality Reduction
- Feature selection

A) 1

B) 2

C) 1 and 2

D) None of these

**Solution: C**

In such case you can use either dimensionality reduction algorithm or the feature selection algorithm

- k-NN is a memory-based approach is that the classifier immediately adapts as we collect new training data.
- The computational complexity for classifying new samples grows linearly with the number of samples in the training dataset in the worst-case scenario.

A) 1

B) 2

C) 1 and 2

D) None of these

**Solution: C**

Both are true and self explanatory

A) k1 > k2> k3

B) k1<k2

C) k1 = k2 = k3

D) None of these**Solution: D**Value of k is highest in k3, whereas in k1 it is lowest

A) 1

B) 2

C) 3

D) 5**Solution: B**

If you keep the value of k as 2, it gives the lowest cross validation accuracy. You can try this out yourself.

**Note: Model has successfully deployed and no technical issues are found at client side except the model performance**

A) It is probably a overfitted model

B) It is probably a underfitted model

C) Can’t say

D) None of these

In an overfitted module, it seems to be performing well on training data, but it is not generalized enough to give the same results on a new data.

- In case of very large value of k, we may include points from other classes into the neighborhood.
- In case of too small value of k the algorithm is very sensitive to noise

A) 1

B) 2

C) 1 and 2

D) None of these

**Solution: C**

Both the options are true and are self explanatory.

A) The classification accuracy is better with larger values of k

B) The decision boundary is smoother with smaller values of k

C) The decision boundary is linear

D) k-NN does not require an explicit training step**Solution: D**

Option A: This is not always true. You have to ensure that the value of k is not too high or not too low.

Option B: This statement is not true. The decision boundary can be a bit jagged

Option C: Same as option B

Option D: This statement is true

A) TRUE

B) FALSE**Solution: A**

You can implement a 2-NN classifier by ensembling 1-NN classifiers

A) The boundary becomes smoother with increasing value of K

B) The boundary becomes smoother with decreasing value of K

C) Smoothness of boundary doesn’t dependent on value of K

D) None of these**Solution: A**The decision boundary would become smoother by increasing the value of K

- We can choose optimal value of k with the help of cross validation
- Euclidean distance treats each feature as equally important

A) 1

B) 2

C) 1 and 2

D) None of these

**Solution: C**

Both the statements are true

**Context 29-30:**

Suppose, you have trained a k-NN model and now you want to get the prediction on test data. Before getting the prediction suppose you want to calculate the time taken by k-NN for predicting the class for test data.**Note: Calculating the distance between 2 observation will take D time.**

A) N*D

B) N*D*2

C) (N*D)/2

D) None of these**Solution: A**

The value of N is very large, so option A is correct

A) 1-NN >2-NN >3-NN

B) 1-NN < 2-NN < 3-NN

C) 1-NN ~ 2-NN ~ 3-NN

D) None of these**Solution: C**

The training time for any value of k inÂ **KNN algorithm**Â is the same.

Here are some resources to get in depth knowledge in the subject.

**Machine Learning Certification Course for Beginners****Essentials of Machine Learning Algorithms (with Python and R Codes)****Simple Guide to Logistic Regression in R****Introduction to k-nearest neighbors : Simplified**

If you are just getting started with Machine Learning and Data Science, here is a course to assist you in your journey to Master Data Science and Machine Learning. Check out the detailed course structure in the link below:

**Understand the Basics:**Before the interview, make sure you have a strong understanding of the basics of the KNN algorithm. Review the key concepts such as distance metrics, k-value selection, and the curse of dimensionality.**Know the Applications:**KNN has a variety of practical applications, including image recognition, recommender systems, and anomaly detection. Make sure you have a good understanding of these applications and how KNN is used in each of them.**Prepare for Technical Questions:**Be prepared to answer technical questions related to KNN, such as how to choose the optimal value of k, how to handle imbalanced data, and how to deal with missing data. Look up KNN interview questions online to get a sense of the types of questions that may be asked.**Demonstrate your Problem-solving Skills:**Be prepared to walk through a problem-solving exercise using KNN. This could include a real-world scenario or a hypothetical problem. Walk the interviewer through your thought process and explain how you would approach the problem using KNN.**Practice, Practice, Practice:**The best way to prepare for a KNN interview is to practice. Search for KNN interview questions and practice answering them. Consider working through example problems or participating in data science competitions to improve your KNN skills.

Being prepared for KNN interview questions is crucial for anyone looking to enter the field of data science or machine learning. Understanding the basics of the KNN algorithm, its practical applications, and how to handle technical questions can help you demonstrate your knowledge and problem-solving skills. By practicing KNN interview questions and working through example problems, you can improve your understanding and feel more confident during the interview process. With these tips in mind, you can approach KNN interviews with confidence and set yourself up for success in your data science career.

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