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**

B) 2

C) 4

D) 8

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

A) 1

B) 2

C) 4

D) 8**Solution: A**

B) 2

C) 4

D) 8

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**

B) 5

C) Both have same

D) None of these

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**

B) 4/14

C) 6/14

D) 8/14

E) None of the above

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**

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

C) Can’t say

D) None of these

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**

B) When you decrease the k the variance will increases

C) Can’t say

D) None of these

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

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**

B) I will decrease the value of k

C) Noise can not be dependent on value of k

D) None of these

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

A) 1

B) 2

C) 3

D) 5

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

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**

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

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**

B) FALSE

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

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

- 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.

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**

B) N*D*2

C) (N*D)/2

D) None of these

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**

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

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

D) None of these

The training time for any value of k in KNN algorithm 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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Become a full stack data scientist
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Understanding Cost Function
Understanding Gradient Descent
Math Behind Gradient Descent
Assumptions of Linear Regression
Implement Linear Regression from Scratch
Train Linear Regression in Python
Implementing Linear Regression in R
Diagnosing Residual Plots in Linear Regression Models
Generalized Linear Models
Introduction to Logistic Regression
Odds Ratio
Implementing Logistic Regression from Scratch
Introduction to Scikit-learn in Python
Train Logistic Regression in python
Multiclass using Logistic Regression
How to use Multinomial and Ordinal Logistic Regression in R ?
Challenges with Linear Regression
Introduction to Regularisation
Implementing Regularisation
Ridge Regression
Lasso Regression

Introduction to Stacking
Implementing Stacking
Variants of Stacking
Implementing Variants of Stacking
Introduction to Blending
Bootstrap Sampling
Introduction to Random Sampling
Hyper-parameters of Random Forest
Implementing Random Forest
Out-of-Bag (OOB) Score in the Random Forest
IPL Team Win Prediction Project Using Machine Learning
Introduction to Boosting
Gradient Boosting Algorithm
Math behind GBM
Implementing GBM in python
Regularized Greedy Forests
Extreme Gradient Boosting
Implementing XGBM in python
Tuning Hyperparameters of XGBoost in Python
Implement XGBM in R/H2O
Adaptive Boosting
Implementing Adaptive Boosing
LightGBM
Implementing LightGBM in Python
Catboost
Implementing Catboost in Python

Introduction to Clustering
Applications of Clustering
Evaluation Metrics for Clustering
Understanding K-Means
Implementation of K-Means in Python
Implementation of K-Means in R
Choosing Right Value for K
Profiling Market Segments using K-Means Clustering
Hierarchical Clustering
Implementation of Hierarchial Clustering
DBSCAN
Defining Similarity between clusters
Build Better and Accurate Clusters with Gaussian Mixture Models

Introduction to Machine Learning Interpretability
Framework and Interpretable Models
model Agnostic Methods for Interpretability
Implementing Interpretable Model
Understanding SHAP
Out-of-Core ML
Introduction to Interpretable Machine Learning Models
Model Agnostic Methods for Interpretability
Game Theory & Shapley Values

Deploying Machine Learning Model using Streamlit
Deploying ML Models in Docker
Deploy Using Streamlit
Deploy on Heroku
Deploy Using Netlify
Introduction to Amazon Sagemaker
Setting up Amazon SageMaker
Using SageMaker Endpoint to Generate Inference
Deploy on Microsoft Azure Cloud
Introduction to Flask for Model
Deploying ML model using Flask

WOW !! Nice Sir, Thankyou.

Answer to Q 12 should be + class. All together there are 8 observation 4 of - and 4 of+. But the nearest ones for 1,1 is +

Hi Sasikanth, There was a typo in the questions. I have updated the same, thanks for the feedback

Lovely test. Please, can I get the soft copy of the questions & answers for both the KNN & TREE Algorithms to my email?: [email protected].

Answers to Q 25 & 27 are bit contradictory

Hi Anubhav, Answer to Q 25 is that the decision boundary can be a bit jagged( not smooth when k is small). Whereas the answer to Q25 is that the decision boundary is smooth for large values of k.

10 is wrong.

Hi Nenad, Answer to question 10 will be 1. As manhattan distance is the sum of absolute difference of the vectors. So it will be [mod(1-2) + mod(3-3)] =1

I am relatively new Data science in python and was exploring some competition on data science, i am getting confused with "Training data Set" and "Test Data Set" . Some projects have merged both and some they have kept separate. What is the rationale behind having two data sets. Any advise will be helpful thanks

Helpful. Even better would be masked answers think spoilers. This would ensure one could scroll without seeing the answer by mistake.