Most Frequently Asked Interview Questions on KNN Algorithm

KNN stands for K-Nearest Neighbors, a supervised machine learning algorithm that can operate with both classification and regression tasks. KNN is often one of the hot topics in the interviews when the panel is interested in someone who can deal with sparse datasets or even the curse of dimensionality. While working with the KNN model as creating a model is not a tedious task, but dealing with its drawbacks and coming back with a solution is! In this article, we’ll discuss some of the most asked and tricky questions related to the KNN algorithm. So let’s get started!

KNN Interview Questions

1. What is KNN?

The K-nearest neighbors (KNN) algorithm is a supervised machine learning method that makes predictions based on how close a data point is to others. It’s widely used for both classification and regression tasks because of its simplicity and popularity.

2. When is the KNN algorithm required?

If KNN is used as the primary model, one needs to have sufficient domain knowledge of the problem statement they’re working on, as the KNN algorithm can give us a high-accuracy model, but the same is not human-readable. Other than that, KNN can work accurately for classification problems where we need to find the data point (say X1) from two categories in the sample space.

Along with classification problems, KNN also fits well with regression tasks. Make sure that this model is not preferred when we have a way too large a dataset to deal with, as KNN is a distance-based algorithm which makes it high in cost when it comes to calculating the distance between two data points.

3. Difference between KNN and K-means?

4. How does KNN approach the assigned task?

KNN follows a well-structured method to complete the assigned task. Here is the general workflow:

• Step 1: The first step is to choose the number of neighbors, i.e., the K-variable, which changes based on the requirements and different tasks.
• Step 2: So, we already have selected the number of neighbors. Now we need to find the Euclidean distance of those neighbors.
• Step 3: After calculating the Euclidean distance between those points, choose the nearest K neighbors based on the previous calculation.
• Step 4: Now, count the total number of data points in both categories from the selected K-neighbors.
• Step 5: The last step is to give the new data points to those categories where the number of K-neighbors is maximum.

5. How to find the best value for K in the KNN algorithm?

First, we need to know what exactly is the K value. K is the numeric value that keeps the count of nearest neighbors, and we can’t have the hit, and trial method for multiple K values as the cost of calculation is pretty expensive hence we need to have certain guidelines to choose the optimal “K.”

• It is quite a domain-specific task that also requires an experience in a related field to choose the optimal K-value for the different problem statements, widely the most preferred value for K is supposed to be 5 (not a hard-coded number).
• If one is choosing a very small value for K (say K=1, 2) for reducing the cost of computation, then it will lead to a noisy model which will surely be prone to outliers in the model.
• Moderately large values for K are preferred, but when it is too large, then it will lead to the underfitting condition.

6. How is KNN different from other classification algorithms in terms of its implementation?

Choosing KNN over another classification algorithm solely depends on our requirements. Suppose we are working on a task that requires flexibility in the model, then we can go for KNN, whereas if efficiency is the priority then we can go for other algorithms like Gradient Descent or Logistic Regression.

Note: Above answer can backfire on another question, so be prepared for it: How KNN is more flexible?

The main logic behind KNN flexibility is that it is a non-parametric algorithm so it won’t have to make any assumption on the underlying dataset. Still, at the same time, it is expensive in computation, unlike other classification algorithms.

7. How are KNN and decision trees different in terms of performance?

Both decision trees and KNN are non-parametric algorithms, but are different in their way of delivering the results. Here are the primary differences:

• When dealing with larger datasets, decision trees are faster than KNN because of their high computational cost (when distance is being calculated).
• KNN is more accurate than decision trees as it scans the whole dataset closely.
• KNN is easy to implement compared to decision trees.

8. Why most of the time Euclidean distance the preferred method for KNN?

For calculating distances in KNN, we have multiple options available, like Minkowski, cosine similarity measure, and so on. But Euclidean distance is a widely preferred method for calculating the distance as it returns us the shortest distance between two data points.

9. Why is data normalization an important step in KNN?

Data Normalization is the process where the whole dataset is scaled down within a specific range, mostly between 0 and 1. This turned out to be a necessary step while dealing with the KNN algorithm, as it is a distance-based algorithm, so if the data points are not within a specific range, then different magnitudes can misclassify the data points in the testing phase.

10. What do you understand by the curse of dimensionality, and how KNN is affected?

The curse of dimensionality is when the dimensions tend to increase, then the data becomes more sparse, i.e., we often find tons of space in the datasets, which leads to the state of overfitting, also making the algorithm incapable of finding the nearest neighbors. Ideally, as the number of dimensions increases, the space in the dataset should also increase exponentially (both should positively complement each other).

Linearization is one of the best techniques to break the curse of dimensionality.

11. What would be the situations where KNN will perform poorly?

There are a few conditions where KNN will not perform based on our expectations, listed below:

1. When the data is very noisy or is not linearly separable.
2. KNN, at times, can be costly in terms of computations for larger datasets.
3. KNN is least preferred when datasets have multiple dimensions, as it leads to the curse of dimensionality.

Conclusion

By far we have discussed the common questions that are frequently asked in an interview related to KNN. This will help you in revision and let you know what things to cover briefly regarding this algorithm. If you’re interested in more questions, you can read through the list of 30 interview questions on KNN.

This article was published as part of the Data Science Blogathon.

Aman Preet